This repository provides a collection of interactive notebooks to the OPERA Products: Coregistered Single-Look Complex (CSLC), Dynamic Surface Water eXtent (DSWx), Land Disturbance (DIST), Radiometric and Terrain Corrected Sentinel-1 (RTC-S1), and Displacement (DISP) products. They contain several Jupyter notebooks that provide introductions and showcase applications of these products including flood mapping, water reservoir monitoring and monitoring wildfire evolution. To get started click the launch Binder logo above. Binder will open the Jupyter notebooks in an executable environment without requiring you to install any new software.
- Software Dependencies and Installation
- Usage: How to Run a Jupyter Notebook
- Jupyter Notebooks
- Key Contributors
This repository can be run by clicking on the Binder logo above or running on your local machine. For the required dependencies, we strongly recommend using Anaconda package manager for easy installation of dependencies in the python environment. Below we outline how to access and manipulate this repository on your local machine using conda.
First, download/clone the repository.
git clone https://github.com/OPERA-Cal-Val/OPERA_Applications
cd OPERA_Applications
Run the commands below to create a new conda environment opera_app and activate it:
conda env create -f environment.yml
conda activate opera_app
Start the notebook server from the command line (using Terminal on Mac/Linux, Command Prompt on Windows) by running:
jupyter notebook
This will print some information about the notebook server in your terminal, including the URL of the web application (by default, http://localhost:8888):
[I 15:55:02.043 NotebookApp] Serving notebooks from local directory: /Users/home/
[I 15:55:02.043 NotebookApp] Jupyter Notebook 6.4.12 is running at: http://localhost:8888/The Jupyter Notebook application will then open in your default web browser to this URL, and you will see the contents of the directory in which the notebook server was started. Select a notebook, and it will open in a secondary browser tab for you to run and explore.
Note: For easy navigation, it is suggested to start a notebook server in the highest level directory in your file system that contains notebooks. See the Jupyter documentation on Running the Notebook for additional details.
The OPERA CSLC-S1 product provides geocoded burst-wise complex data containing both the amplitude and phase information from Sentinel-1 (S1). More information about OPERA CSLC-S1 is available at https://www.jpl.nasa.gov/go/opera/products/cslc-product-suite. Also refer to the CSLC Product white paper [here] for high-level information.
Below describes the subdirectories within the CSLC folder.
This discover directory contains Jupyter notebooks that showcase how to interface with CSLC products.
.
├── ...
├── Discover
│ └── Create_Interferogram_by_streaming_CSLC-S1.ipynb # Access CSLC via S3
└── ...
The OPERA DSWx product maps pixel-wise surface water detections using the Harmonized Landsat-8 Sentinel-2 A/B (HLS) data. More information about OPERA DSWx is available at https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite. Also refer to the DSWx Product white paper [here] for high-level information.
Below describes the subdirectories within the DSWx folder.
This discover directory contains Jupyter notebooks that showcase how to interface with DSWx products.
.
├── ...
├── Discover
│ ├── Stream_DSWx-HLS_HTTPSvsS3.ipynb # Access DSWx via HTTPs and S3
│ ├── Stream_and_Viz_DSWx-HLS_viaCMR-STAC.ipynb # Access DSWx via CMR-STAC
│ └── Stream_and_Viz_DSWx-HLS_viaDirectHTTPS.ipynb # Access DSWx via Direct HTTPS
└── ...
The flood directory contains a Jupyter notebook that generates flood maps using provisional DSWx products over Pakistan.
.
├── ...
├── Flood
│ └── DSWx_FloodProduct.ipynb # Create flood map using DSWx from the cloud
└── ...
This mosaics directory demonstrates how PO.DAAC can be programmatically queried for DSWx data over a given region, for a specified time period. The returned DSWx granules are mosaicked to return a single raster image. As motivating examples, we demonstrate this over the state of California and the entireity of Australia.
.
├── ...
├── Mosaics
│ ├── notebooks
│ │ └── Create-mosaics.ipynb # Notebook to query PO.DAAC for DSWx data and mosaic returned granules
│ ├── data
│ │ ├── shapefiles # Shapefiles used to query PO.DAAC
│ │ ├── australia # Folder containing example mosaicked raster over Australia
│ │ └── california # Folder containing example mosaicked raster over CA
│ ├── README.md
│ └── environment.yml # YAML file containing dependencies needed to run code in this folder
└── ...
This reservoir directory contains Jupyter notebooks that demonstrate reservoir monitoring applications of provisional DSWx products over Lake Mead, NV.
.
├── ...
├── Reservoir
│ ├── Intro_to_DSWx.ipynb # Highlights four main layers of DSWx products
│ ├── Reservoir_Monitoring.ipynb # Reservoir monitoring of Lake Mead, NV between 2014-2022
│ ├── Time_Slider_Visualization.ipynb # Visualization of DSWx of Lake Mead, NV for the year 2022
│ └── aux_files
│ ├── T11SQA_manifest.txt # S3 links to provisional products
│ ├── prep_shapefile.ipynb # Create buffered shapefile
│ ├── lakebnds/ # 2003 Lake Mead lake bounds shapefile
│ └── bufferlakebnds/ # Buffered Lake Mead lake bounds shapefile
└── ...
The OPERA DIST product maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat-8 Sentinel-2 A/B (HLS) data. More information about OPERA DIST is available at https://www.jpl.nasa.gov/go/opera/products/dist-product-suite. Also refer to the DIST Product white paper [here] for high-level information.
Below describes the subdirectories within the DIST folder.
This discover directory contains Jupyter notebooks that showcase how to interface with DIST products.
.
├── ...
├── Discover
│ ├── Stream_and_Viz_DIST-ALERT-folium.ipynb # Access DIST-ALERT via CMR-STAC
│ └── Stream_and_Viz_DIST_Functions.py # DIST functions
└── ...
This wildfire directory contains Jupyter notebooks that demonstrate widlfire applicaitons of DIST products.
.
├── ...
├── Wildfire
│ ├── Intro_to_DIST.ipynb # Highlights three main layers of DIST products
│ ├── McKinney.ipynb # Visualization of 2022 McKinney wildfire with DIST
│ └── aux_files
│ └── McKinney_NIFC # Perimeter of 2022 McKinney wildfire
└── ...
The RTC-S1 product is a Level 2 product that contains Sentinel-1 backscatter normalized with respect to the topography and projected onto pre-defined UTM/Polar stereographic map projection systems. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product. The RTC product maps signals largely related to the physical properties of the ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The product is provided in a GeoTIFF file format and has a resolution of 30 m. All products will be accessible through the Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC).
Below describes the subdirectories within the RTC folder.
.
├── ...
├── notebooks
│ ├── RTC_notebook.ipynb # Notebook demonstrating streaming, mosaicking, and visualizing RTC data
│ └── rtc_utils.py # helper functions for notebook
├── README.md
└── ...
- Mary Grace Bato
- Kelly Devlin
- Rubie Dhillon
- Karthik Venkataramani
- Cole Speed