diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index ac4b859cf..ed2a47f6b 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -42,7 +42,7 @@ jobs:
         run: |
           echo "PYTHON_VERSION=${{ matrix.python-version }}" >> $GITHUB_ENV
       - name: Set up Python
-        uses: actions/setup-python@v5.0.0
+        uses: actions/setup-python@v5.1.0
         with:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
diff --git a/.github/workflows/packit.yaml b/.github/workflows/packit.yaml
index e6ed0b9c6..4f032690b 100644
--- a/.github/workflows/packit.yaml
+++ b/.github/workflows/packit.yaml
@@ -20,7 +20,7 @@ jobs:
         fetch-depth: 0
 
     - name: Set up Python
-      uses: actions/setup-python@v5.0.0
+      uses: actions/setup-python@v5.1.0
       with:
         python-version: 3.9
 
@@ -52,7 +52,7 @@ jobs:
     needs: build-artifact
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/setup-python@v5.0.0
+    - uses: actions/setup-python@v5.1.0
       name: Install Python
       with:
         python-version: 3.9
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 0e94524cf..868779862 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -35,7 +35,7 @@ jobs:
         with:
           fetch-depth: 0 # Fetch all history for all branches and tags.
       - name: Set up Python
-        uses: actions/setup-python@v5.0.0
+        uses: actions/setup-python@v5.1.0
         with:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml
index 6ebab0acf..feb69c488 100644
--- a/.github/workflows/pypi.yaml
+++ b/.github/workflows/pypi.yaml
@@ -24,7 +24,7 @@ jobs:
         fetch-depth: 0
 
     - name: Set up Python
-      uses: actions/setup-python@v5.0.0
+      uses: actions/setup-python@v5.1.0
       with:
         python-version: 3.9
 
@@ -56,7 +56,7 @@ jobs:
     needs: build-artifact
     runs-on: ubuntu-20.04
     steps:
-    - uses: actions/setup-python@v5.0.0
+    - uses: actions/setup-python@v5.1.0
       name: Install Python
       with:
         python-version: 3.9
diff --git a/.github/workflows/windows.yaml b/.github/workflows/windows.yaml
index b77576831..bc5e1bce9 100644
--- a/.github/workflows/windows.yaml
+++ b/.github/workflows/windows.yaml
@@ -46,7 +46,7 @@ jobs:
             # Check data endpoint
             curl http://localhost:8080/data/
         - name: Setup Python
-          uses: actions/setup-python@v5.0.0
+          uses: actions/setup-python@v5.1.0
           with:
             python-version: ${{ matrix.python-version }}
             architecture: x64
diff --git a/echopype/mask/api.py b/echopype/mask/api.py
index 71b618a1a..e8cf10fa6 100644
--- a/echopype/mask/api.py
+++ b/echopype/mask/api.py
@@ -105,10 +105,26 @@ def _validate_and_collect_mask_input(
             # the coordinate sequence matters, so fix the tuple form
             allowed_dims = [
                 ("ping_time", "range_sample"),
+                ("ping_time", "depth"),
                 ("channel", "ping_time", "range_sample"),
+                ("channel", "ping_time", "depth"),
             ]
             if mask[mask_ind].dims not in allowed_dims:
-                raise ValueError("All masks must have dimensions ('ping_time', 'range_sample')!")
+                raise ValueError(
+                    "Masks must have one of the following dimensions: "
+                    "('ping_time', 'range_sample'), ('ping_time', 'depth'), "
+                    "('channel', 'ping_time', 'range_sample'), "
+                    "('channel', 'ping_time', 'depth')"
+                )
+
+        # Check for the channel dimension consistency
+        channel_dim_shapes = set()
+        for mask_indiv in mask:
+            if "channel" in mask_indiv.dims:
+                for mask_chan_ind in range(len(mask_indiv["channel"])):
+                    channel_dim_shapes.add(mask_indiv.isel(channel=mask_chan_ind).shape)
+        if len(channel_dim_shapes) > 1:
+            raise ValueError("All masks must have the same shape in the 'channel' dimension.")
 
     else:
         if not isinstance(storage_options_mask, dict):
@@ -128,7 +144,7 @@ def _validate_and_collect_mask_input(
 
 
 def _check_var_name_fill_value(
-    source_ds: xr.Dataset, var_name: str, fill_value: Union[int, float, np.ndarray, xr.DataArray]
+    source_ds: xr.Dataset, var_name: str, fill_value: Union[int, float, xr.DataArray]
 ) -> Union[int, float, np.ndarray, xr.DataArray]:
     """
     Ensures that the inputs ``var_name`` and ``fill_value`` for the function
@@ -140,12 +156,12 @@ def _check_var_name_fill_value(
         A Dataset that contains the variable ``var_name``
     var_name: str
         The variable name in ``source_ds`` that the mask should be applied to
-    fill_value: int or float or np.ndarray or xr.DataArray
+    fill_value: int, float, or xr.DataArray
         Specifies the value(s) at false indices
 
     Returns
     -------
-    fill_value: int or float or np.ndarray or xr.DataArray
+    fill_value: int, float, or xr.DataArray
         fill_value with sanitized dimensions
 
     Raises
@@ -167,17 +183,12 @@ def _check_var_name_fill_value(
         raise ValueError("The Dataset source_ds does not contain the variable var_name!")
 
     # check the type of fill_value
-    if not isinstance(fill_value, (int, float, np.ndarray, xr.DataArray)):
-        raise TypeError(
-            "The input fill_value must be of type int or " "float or np.ndarray or xr.DataArray!"
-        )
+    if not isinstance(fill_value, (int, float, xr.DataArray)):
+        raise TypeError("The input fill_value must be of type int, float, or xr.DataArray!")
 
     # make sure that fill_values is the same shape as var_name
-    if isinstance(fill_value, (np.ndarray, xr.DataArray)):
-        if isinstance(fill_value, xr.DataArray):
-            fill_value = fill_value.data.squeeze()  # squeeze out length=1 channel dimension
-        elif isinstance(fill_value, np.ndarray):
-            fill_value = fill_value.squeeze()  # squeeze out length=1 channel dimension
+    if isinstance(fill_value, xr.DataArray):
+        fill_value = fill_value.data.squeeze()  # squeeze out length=1 channel dimension
 
         source_ds_shape = (
             source_ds[var_name].isel(channel=0).shape
@@ -248,7 +259,7 @@ def apply_mask(
     source_ds: Union[xr.Dataset, str, pathlib.Path],
     mask: Union[xr.DataArray, str, pathlib.Path, List[Union[xr.DataArray, str, pathlib.Path]]],
     var_name: str = "Sv",
-    fill_value: Union[int, float, np.ndarray, xr.DataArray] = np.nan,
+    fill_value: Union[int, float, xr.DataArray] = np.nan,
     storage_options_ds: dict = {},
     storage_options_mask: Union[dict, List[dict]] = {},
 ) -> xr.Dataset:
@@ -256,29 +267,45 @@ def apply_mask(
     Applies the provided mask(s) to the Sv variable ``var_name``
     in the provided Dataset ``source_ds``.
 
+    The code allows for these 3 cases of `source_ds` and `mask` dimensions:
+
+    1) No channel in both `source_ds` and `mask`,
+    but they have matching `ping_time` and
+    `depth` (or `range_sample`) dimensions.
+    2) `source_ds` and `mask` both have matching `channel`,
+    `ping_time`, and `depth` (or `range_sample`) dimensions.
+    3) `source_ds` has the channel dimension and `mask` doesn't,
+    but they have matching
+    `ping_time` and `depth` (or `range_sample`) dimensions.
+
+    If a user only wants to apply masks to a subset of the channels in `source_ds`,
+    they could put 1s to allow all data entries in the other channels.
+
     Parameters
     ----------
     source_ds: xr.Dataset, str, or pathlib.Path
         Points to a Dataset that contains the variable the mask should be applied to
     mask: xr.DataArray, str, pathlib.Path, or a list of these datatypes
         The mask(s) to be applied.
-        Can be a single input or list that corresponds to a DataArray or a path.
-        Each entry in the list must have dimensions ``('ping_time', 'range_sample')``.
-        Multi-channel masks are not currently supported.
+        Can be a individual input or a list that corresponds to a DataArray or a path.
+        Each individual input or entry in the list must contain dimensions
+        ``('ping_time', 'range_sample')`` or dimensions ``('ping_time', 'depth')``.
+        The mask can also contain the dimension ``channel``.
         If a path is provided this should point to a zarr or netcdf file with only
         one data variable in it.
         If the input ``mask`` is a list, a logical AND will be used to produce the final
         mask that will be applied to ``var_name``.
     var_name: str, default="Sv"
         The Sv variable name in ``source_ds`` that the mask should be applied to.
-        This variable needs to have coordinates ``ping_time`` and ``range_sample``,
-        and can optionally also have coordinate ``channel``.
+        This variable needs to have coordinates ``('ping_time', 'range_sample')`` or
+        coordinates ``('ping_time', 'depth')``, and can optionally also have coordinate
+        ``channel``.
         In the case of a multi-channel Sv data variable, the ``mask`` will be broadcast
         to all channels.
-    fill_value: int, float, np.ndarray, or xr.DataArray, default=np.nan
+    fill_value: int, float, or xr.DataArray, default=np.nan
         Value(s) at masked indices.
-        If ``fill_value`` is of type ``np.ndarray`` or ``xr.DataArray``,
-        it must have the same shape as each entry of ``mask``.
+        If ``fill_value`` is of type ``xr.DataArray`` it must have the same shape as each
+        entry of ``mask``.
     storage_options_ds: dict, default={}
         Any additional parameters for the storage backend, corresponding to the
         path provided for ``source_ds``
@@ -305,43 +332,49 @@ def apply_mask(
 
     # Obtain final mask to be applied to var_name
     if isinstance(mask, list):
-        # perform a logical AND element-wise operation across the masks
-        final_mask = np.logical_and.reduce(mask)
+        # Broadcast all input masks together before combining them
+        broadcasted_masks = xr.broadcast(*mask)
+
+        # Perform a logical AND element-wise operation across the masks
+        final_mask = np.logical_and.reduce(broadcasted_masks)
 
         # xr.where has issues with attrs when final_mask is an array, so we make it a DataArray
-        final_mask = xr.DataArray(final_mask, coords=mask[0].coords)
+        final_mask = xr.DataArray(final_mask, coords=broadcasted_masks[0].coords)
     else:
         final_mask = mask
 
-    # Sanity check: final_mask should be of the same shape as source_ds[var_name]
-    #               along the ping_time and range_sample dimensions
-    def get_ch_shape(da):
-        return da.isel(channel=0).shape if "channel" in da.dims else da.shape
-
-    # Below operate on the actual data array to be masked
+    # Operate on the actual data array to be masked
     source_da = source_ds[var_name]
 
-    source_da_shape = get_ch_shape(source_da)
-    final_mask_shape = get_ch_shape(final_mask)
-
-    if final_mask_shape != source_da_shape:
+    # The final_mask should be of the same shape as source_ds[var_name]
+    # along the ping_time and range_sample dimensions.
+    source_da_chan_shape = (
+        source_da.isel(channel=0).shape if "channel" in source_da.dims else source_da.shape
+    )
+    final_mask_chan_shape = (
+        final_mask.isel(channel=0).shape if "channel" in final_mask.dims else final_mask.shape
+    )
+    if final_mask_chan_shape != source_da_chan_shape:
         raise ValueError(
             f"The final constructed mask is not of the same shape as source_ds[{var_name}] "
-            "along the ping_time and range_sample dimensions!"
+            "along the ping_time, and range_sample dimensions!"
         )
-
-    # final_mask is always an xr.DataArray with at most length=1 channel dimension
-    if "channel" in final_mask.dims:
-        final_mask = final_mask.isel(channel=0)
-
-    # Make sure fill_value and final_mask are expanded in dimensions
-    if "channel" in source_da.dims:
-        if isinstance(fill_value, np.ndarray):
-            fill_value = np.array([fill_value] * source_da["channel"].size)
-        final_mask = np.array([final_mask.data] * source_da["channel"].size)
+    # If final_mask has dim channel then source_da must have dim channel
+    if "channel" in final_mask.dims and "channel" not in source_da.dims:
+        raise ValueError(
+            "The final constructed mask has the channel dimension, "
+            f"so source_ds[{var_name}] must also have the channel dimension."
+        )
+    # If final_mask and source_da both have channel dimension, then they must
+    # have the same number of channels.
+    elif "channel" in final_mask.dims and "channel" in source_da.dims:
+        if len(final_mask["channel"]) != len(source_da["channel"]):
+            raise ValueError(
+                f"If both the final constructed mask and source_ds[{var_name}] "
+                "have the channel dimension, that dimension should match between the two."
+            )
 
     # Apply the mask to var_name
-    # Somehow keep_attrs=True errors out here, so will attach later
     var_name_masked = xr.where(final_mask, x=source_da, y=fill_value)
 
     # Obtain a shallow copy of source_ds
@@ -356,12 +389,11 @@ def get_ch_shape(da):
         _variable_prov_attrs(output_ds[var_name], mask)
     )
 
+    # Attribute handling
     process_type = "mask"
     prov_dict = echopype_prov_attrs(process_type=process_type)
     prov_dict[f"{process_type}_function"] = "mask.apply_mask"
-
     output_ds = output_ds.assign_attrs(prov_dict)
-
     output_ds = insert_input_processing_level(output_ds, input_ds=source_ds)
 
     return output_ds
diff --git a/echopype/tests/mask/test_mask.py b/echopype/tests/mask/test_mask.py
index d056cb3b3..82fe75932 100644
--- a/echopype/tests/mask/test_mask.py
+++ b/echopype/tests/mask/test_mask.py
@@ -134,6 +134,7 @@ def get_mock_source_ds_apply_mask(n: int, n_chan: int, is_delayed: bool) -> xr.D
     -------
     xr.Dataset
         A Dataset containing data variables ``var1, var2`` with coordinates
+        ``('channel', 'ping_time', 'depth')`` and
         ``('channel', 'ping_time', 'range_sample')``.
         The variables are square matrices of ones for each ``channel``.
     """
@@ -148,24 +149,15 @@ def get_mock_source_ds_apply_mask(n: int, n_chan: int, is_delayed: bool) -> xr.D
         mock_var_data = [np.ones((n, n)) for i in range(n_chan)]
 
     # create mock var1 and var2 DataArrays
-    mock_var1_da = xr.DataArray(
-        data=np.stack(mock_var_data),
-        coords={
-            "channel": ("channel", chan_vals, {"long_name": "channel name"}),
-            "ping_time": np.arange(n),
-            "range_sample": np.arange(n),
-        },
-        attrs={"long_name": "variable 1"},
-    )
-    mock_var2_da = xr.DataArray(
-        data=np.stack(mock_var_data),
-        coords={
-            "channel": ("channel", chan_vals, {"long_name": "channel name"}),
-            "ping_time": np.arange(n),
-            "range_sample": np.arange(n),
-        },
-        attrs={"long_name": "variable 2"},
-    )
+    mock_var1_da = xr.DataArray(data=np.stack(mock_var_data),
+                                coords={"channel": ("channel", chan_vals, {"long_name": "channel name"}),
+                                        "ping_time": np.arange(n), "depth": np.arange(n)},
+                                attrs={"long_name": "variable 1"})
+    mock_var2_da = xr.DataArray(data=np.stack(mock_var_data),
+                                coords={"channel": ("channel", chan_vals, {"long_name": "channel name"}),
+                                        "ping_time": np.arange(n),
+                                        "range_sample": np.arange(n)},
+                                attrs={"long_name": "variable 2"})
 
     # create mock Dataset
     mock_ds = xr.Dataset(data_vars={"var1": mock_var1_da, "var2": mock_var2_da})
@@ -805,82 +797,79 @@ def test_validate_and_collect_mask_input(
         assert mask_out.identical(mask_da)
 
 
+@pytest.mark.parametrize(
+    ("mask_list"),
+    [
+    pytest.param(
+        [xr.DataArray([np.identity(4)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0']})]
+    ),
+    pytest.param(
+        [xr.DataArray([np.identity(4), np.identity(4)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0', 'channel_1']})]
+    ),
+    pytest.param(
+        [xr.DataArray([np.identity(4), np.identity(4)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0', 'channel_1']}),
+        xr.DataArray([np.identity(4), np.identity(4)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0', 'channel_1']})]
+    ),
+    pytest.param(
+        [xr.DataArray([np.identity(3), np.identity(3)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0', 'channel_1']}),
+        xr.DataArray([np.identity(4), np.identity(4)], dims=['channel', 'ping_time', 'depth'],
+                       coords={'channel': ['channel_0', 'channel_1']})],
+        marks=pytest.mark.xfail(
+            strict=True,
+            reason="This should fail because the channel dims are not uniform."
+        ))
+    ],
+    ids=["single_channel_mask", "double_channel", "double_channel_double_masks",
+         "inconsistent_channels_across_two_masks"]
+)
+def test_multi_mask_validate_and_collect_mask(mask_list: List[xr.DataArray]):
+    """
+    Tests the allowable types and dimensions for multimask input.
+
+    Parameters
+    ----------
+    mask_list: List[xr.DataArray]
+        Multimask input to be tested in validate and collect mask input.
+    """
+
+    _validate_and_collect_mask_input(mask=mask_list, storage_options_mask={})
+
+
 @pytest.mark.parametrize(
     ("n", "n_chan", "var_name", "fill_value"),
     [
-        pytest.param(
-            4,
-            2,
-            2.0,
-            np.nan,
-            marks=pytest.mark.xfail(
-                strict=True, reason="This should fail because the var_name is not a string."
-            ),
-        ),
-        pytest.param(
-            4,
-            2,
-            "var3",
-            np.nan,
-            marks=pytest.mark.xfail(
-                strict=True,
-                reason="This should fail because mock_ds will " "not have var_name=var3 in it.",
-            ),
-        ),
-        pytest.param(
-            4,
-            2,
-            "var1",
-            "1.0",
-            marks=pytest.mark.xfail(
-                strict=True, reason="This should fail because fill_value is an incorrect type."
-            ),
-        ),
+        pytest.param(4, 2, 2.0, np.nan,
+                     marks=pytest.mark.xfail(strict=True,
+                                             reason="This should fail because the var_name is not a string.")),
+        pytest.param(4, 2, "var3", np.nan,
+                     marks=pytest.mark.xfail(strict=True,
+                                             reason="This should fail because mock_ds will "
+                                                    "not have var_name=var3 in it.")),
+        pytest.param(4, 2, "var2", "1.0",
+                     marks=pytest.mark.xfail(strict=True,
+                                             reason="This should fail because fill_value is an incorrect type.")),
         (4, 2, "var1", 1),
         (4, 2, "var1", 1.0),
-        (2, 1, "var1", np.identity(2)[None, :]),
-        (
-            2,
-            1,
-            "var1",
-            xr.DataArray(
-                data=np.array([[[1.0, 0], [0, 1]]]),
-                coords={"channel": ["chan1"], "ping_time": [0, 1], "range_sample": [0, 1]},
-            ),
-        ),
-        pytest.param(
-            4,
-            2,
-            "var1",
-            np.identity(2),
-            marks=pytest.mark.xfail(
-                strict=True, reason="This should fail because fill_value is not the right shape."
-            ),
-        ),
-        pytest.param(
-            4,
-            2,
-            "var1",
-            xr.DataArray(
-                data=np.array([[1.0, 0], [0, 1]]),
-                coords={"ping_time": [0, 1], "range_sample": [0, 1]},
-            ),
-            marks=pytest.mark.xfail(
-                strict=True, reason="This should fail because fill_value is not the right shape."
-            ),
-        ),
-    ],
-    ids=[
-        "wrong_var_name_type",
-        "no_var_name_ds",
-        "wrong_fill_value_type",
-        "fill_value_int",
-        "fill_value_float",
-        "fill_value_np_array",
-        "fill_value_DataArray",
-        "fill_value_np_array_wrong_shape",
-        "fill_value_DataArray_wrong_shape",
+        pytest.param(2, 1, "var1", np.identity(2)[None, :],
+                     marks=pytest.mark.xfail(strict=True,
+                     reason="This should fail because fill_value is an incorrect type.")),
+        (2, 1, "var1", xr.DataArray(data=np.array([[[1.0, 0], [0, 1]]]),
+                                    coords={"channel": ["chan1"], "ping_time": [0, 1], "depth": [0, 1]})
+         ),          
+        pytest.param(4, 2, "var2",
+                     xr.DataArray(data=np.array([[1.0, 0], [0, 1]]),
+                                  coords={"ping_time": [0, 1], "range_sample": [0, 1]}),
+                     marks=pytest.mark.xfail(strict=True,
+                                             reason="This should fail because fill_value is not the right shape.")),
     ],
+    ids=["wrong_var_name_type", "no_var_name_ds", "wrong_fill_value_type", "fill_value_int",
+         "fill_value_float", "fill_value_np_array", "fill_value_DataArray",
+         "fill_value_DataArray_wrong_shape"]
 )
 def test_check_var_name_fill_value(
     n: int, n_chan: int, var_name: str, fill_value: Union[int, float, np.ndarray, xr.DataArray]
@@ -947,17 +936,11 @@ def test_check_var_name_fill_value(
         # single_mask_float_fill
         (2, 1, "var1", np.identity(2), None, 2.0, False, np.array([[1, 2.0], [2.0, 1]]), False),
         # single_mask_np_array_fill
-        (
-            2,
-            1,
-            "var1",
-            np.identity(2),
-            None,
-            np.array([[[np.nan, np.nan], [np.nan, np.nan]]]),
-            False,
-            np.array([[1, np.nan], [np.nan, 1]]),
-            False,
-        ),
+        pytest.param(
+            2, 1, "var1", np.identity(2), None, np.array([[[np.nan, np.nan], [np.nan, np.nan]]]),
+            False, np.array([[1, np.nan], [np.nan, 1]]), False,
+            marks=pytest.mark.xfail(strict=True,
+            reason="This should fail because fill_value is an incorrect type.")),
         # single_mask_DataArray_fill
         (
             2,
@@ -1078,7 +1061,7 @@ def test_apply_mask(
     mock_ds = get_mock_source_ds_apply_mask(n, n_chan, is_delayed)
 
     # create input mask and obtain temporary directory, if it was created
-    mask, temp_dir = create_input_mask(mask, mask_file, mock_ds.coords)
+    mask, temp_dir = create_input_mask(mask, mask_file, mock_ds[var_name].coords)
 
     # create DataArray form of the known truth value
     var_masked_truth = xr.DataArray(
@@ -1115,61 +1098,146 @@ def test_apply_mask(
         temp_dir.cleanup()
 
 
+@pytest.mark.integration
 @pytest.mark.parametrize(
-    ("source_has_ch", "mask_has_ch"),
-    [
-        (True, True),
-        (False, True),
-        (True, False),
-        (False, False),
+    ("source_has_ch", "mask", "truth_da"),
+    [   
+        # source_with_ch_mask_list_with_ch
+        (True, [
+        xr.DataArray(
+            np.array([np.identity(2), np.identity(2)]),
+            coords={"channel": ["chan1", "chan2"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        xr.DataArray(
+            np.array([np.zeros_like(np.identity(2))]),
+            coords={"channel": ["chan3"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        ],
+        xr.DataArray(
+            np.array([[[1, np.nan], [np.nan, 1]],
+                     [[1, np.nan], [np.nan, 1]],
+                     [[np.nan, np.nan], [np.nan, np.nan]]]),
+            coords={"channel": ["chan1", "chan2", "chan3"],
+                    "ping_time": np.arange(2), "depth": np.arange(2)},
+        )),
+
+        # source_with_ch_mask_list_with_ch_fail_different_channel_lengths
+        (True, [
+        xr.DataArray(
+            np.array([np.identity(2)]),
+            coords={"channel": ["chan1"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        xr.DataArray(
+            np.array([np.zeros_like(np.identity(2))]),
+            coords={"channel": ["chan3"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        ],
+        None),
+
+        # source_with_ch_mask_with_ch
+        (True,
+        xr.DataArray(
+            np.array([np.identity(2), np.identity(2), np.ones_like(np.identity(2))]),
+            coords={"channel": ["chan1", "chan2", "chan3"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        xr.DataArray(
+            np.array([[[1, np.nan], [np.nan, 1]],
+                     [[1, np.nan], [np.nan, 1]],
+                     [[1, 1], [1, 1]]]),
+            coords={"channel": ["chan1", "chan2", "chan3"],
+                    "ping_time": np.arange(2), "depth": np.arange(2)},
+        )),
+
+        # source_with_ch_mask_no_ch
+        (True, xr.DataArray(
+            np.identity(2),
+            coords={"ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_no_channel"},
+        ),
+        xr.DataArray(
+            np.array([[[1, 1, 1], [np.nan, np.nan, np.nan]],
+                      [[np.nan, np.nan, np.nan], [1, 1, 1]]]),
+            coords={"ping_time": np.arange(2), "depth": np.arange(2),
+                    "channel": ["chan1", "chan2", "chan3"]}
+        )),
+
+        # source_no_ch_mask_with_ch_fail
+        (False, xr.DataArray(
+            np.array([np.identity(2)]),
+            coords={"channel": ["chan1"], "ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_with_channel"},
+        ),
+        None),
+
+        # source_no_ch_mask_no_ch
+        (False, xr.DataArray(
+            np.identity(2),
+            coords={"ping_time": np.arange(2), "depth": np.arange(2)},
+            attrs={"long_name": "mask_no_channel"},
+        ),
+        xr.DataArray(
+            np.array([[1, np.nan], [np.nan, 1]]),
+            coords={"ping_time": np.arange(2), "depth": np.arange(2)}
+        )),
+
+        # source_no_ch_mask_no_ch_fail_different_ping_time_depth_shape
+        (False, xr.DataArray(
+            np.zeros((3, 1)),
+            coords={"ping_time": np.arange(3), "depth": np.arange(1)},
+            attrs={"long_name": "mask_no_channel"},
+        ),
+        None),
     ],
     ids=[
+        "source_with_ch_mask_list_with_ch",
+        "source_with_ch_mask_list_with_ch_fail_different_channel_lengths",
         "source_with_ch_mask_with_ch",
-        "source_no_ch_mask_with_ch",
         "source_with_ch_mask_no_ch",
+        "source_no_ch_mask_with_ch_fail",
         "source_no_ch_mask_no_ch",
-    ],
+        "source_no_ch_mask_no_ch_fail_different_ping_time_depth_shape",
+    ]
 )
-def test_apply_mask_channel_variation(source_has_ch, mask_has_ch):
+def test_apply_mask_channel_variation(source_has_ch, mask, truth_da):
+
+    # Create source dataset
     source_ds = get_mock_source_ds_apply_mask(2, 3, False)
     var_name = "var1"
 
-    if mask_has_ch:
-        mask = xr.DataArray(
-            np.array([np.identity(2)]),
-            coords={"channel": ["chA"], "ping_time": np.arange(2), "range_sample": np.arange(2)},
-            attrs={"long_name": "mask_with_channel"},
-        )
-    else:
-        mask = xr.DataArray(
-            np.identity(2),
-            coords={"ping_time": np.arange(2), "range_sample": np.arange(2)},
-            attrs={"long_name": "mask_no_channel"},
-        )
-
-    if source_has_ch:
-        masked_ds = echopype.mask.apply_mask(source_ds, mask, var_name)
-    else:
-        source_ds[f"{var_name}_ch0"] = source_ds[var_name].isel(channel=0).squeeze()
-        var_name = f"{var_name}_ch0"
-        masked_ds = echopype.mask.apply_mask(source_ds, mask, var_name)
-
-    # Output dimension will be the same as source
-    if source_has_ch:
-        truth_da = xr.DataArray(
-            np.array([[[1, np.nan], [np.nan, 1]]] * 3),
-            coords={
-                "channel": ["chan1", "chan2", "chan3"],
-                "ping_time": np.arange(2),
-                "range_sample": np.arange(2),
-            },
-            attrs=source_ds[var_name].attrs,
-        )
+    if truth_da is None:
+        # Attempt to apply mask w/ 'bad' shapes and check for raised ValueError
+        with pytest.raises(ValueError):
+            if source_has_ch:
+                masked_ds = echopype.mask.apply_mask(source_ds,
+                                                    mask,
+                                                    var_name
+                                                    )
+            else:
+                source_ds[f"{var_name}_ch0"] = source_ds[var_name].isel(channel=0).squeeze()
+                var_name = f"{var_name}_ch0"
+                masked_ds = echopype.mask.apply_mask(source_ds,
+                                                    mask,
+                                                    var_name
+                                                    )
     else:
-        truth_da = xr.DataArray(
-            [[1, np.nan], [np.nan, 1]],
-            coords={"ping_time": np.arange(2), "range_sample": np.arange(2)},
-            attrs=source_ds[var_name].attrs,
-        )
-
-    assert masked_ds[var_name].equals(truth_da)
+        # Apply mask and check matching truth_da
+        if source_has_ch:
+            masked_ds = echopype.mask.apply_mask(source_ds,
+                                                mask,
+                                                var_name
+                                                )
+        else:
+            source_ds[f"{var_name}_ch0"] = source_ds[var_name].isel(channel=0).squeeze()
+            var_name = f"{var_name}_ch0"
+            masked_ds = echopype.mask.apply_mask(source_ds,
+                                                mask,
+                                                var_name
+                                                )
+        
+        # Check mask to match truth
+        assert masked_ds[var_name].equals(truth_da)