diff --git a/src/eva/data/gsi_obs_space.py b/src/eva/data/gsi_obs_space.py
index 2c29d203..6bebdac0 100644
--- a/src/eva/data/gsi_obs_space.py
+++ b/src/eva/data/gsi_obs_space.py
@@ -20,25 +20,6 @@
 # --------------------------------------------------------------------------------------------------
 
 
-def all_equal(iterable):
-
-    """
-    Check if all elements in an iterable are equal.
-
-    Args:
-        iterable: An iterable object to check.
-
-    Returns:
-        bool: True if all elements are equal, False otherwise.
-    """
-
-    g = groupby(iterable)
-    return next(g, True) and not next(g, False)
-
-
-# --------------------------------------------------------------------------------------------------
-
-
 def uv(group_vars):
 
     """
@@ -112,30 +93,63 @@ def subset_channels(ds, channels, logger, add_channels_variable=False):
 # --------------------------------------------------------------------------------------------------
 
 
-def satellite_dataset(ds):
+def satellite_dataset(ds, group_vars, force_reshape_all):
 
     """
     Build a new dataset to reshape satellite data.
 
     Args:
         ds (Dataset): The input xarray Dataset.
-
+        group_vars: all or use selected group_vars
+        force_reshape_all: bool that allows user to force all variables to be reshaped
+                           vs. checking whether to thin or reshape
     Returns:
         Dataset: Reshaped xarray Dataset.
     """
 
-    nchans = ds.dims['nchans']
-    iters = int(ds.dims['nobs']/nchans)
+    nchans = ds.sizes['nchans']
+    iters = int(ds.sizes['nobs']/nchans)
 
     coords = {
         'nchans': (('nchans'), ds['sensor_chan'].data),
         'nobs': (('nobs'), np.arange(0, iters)),
     }
-
     data_vars = {}
+
+    required_variables = group_vars[:]
+    if ('sensor_chan' not in required_variables):
+        required_variables.append('sensor_chan')
+
+    lat_lon = ['Latitude', 'Longitude']
+    # these inherently have a spectral dimension.
+    always_2d = ['QC_Flag',
+                 'Observation',
+                 'Obs_Minus_Forecast_adjusted',
+                 'Obs_Minus_Forecast_unadjusted',
+                 'Forecast_adjusted_clear',
+                 'Forecast_unadjusted_clear',
+                 'Forecast_adjusted',
+                 'Forecast_unadjusted',
+                 'Emissivity',
+                 'BC_Scan_Angle',
+                 'BC_Cloud_Liquid_Water',
+                 'BC_Cosine_Latitude_times_Node',
+                 'BC_Sine_Latitude',
+                 'Bias_Correction',
+                 'Bias_Correction_Constant',
+                 'Bias_Correction_ScanAngle',
+                 'Inverse_Observation_Error',
+                 'Input_Observation_Error',
+                 'BC_Constant',
+                 'BC_Lapse_Rate_Squared',
+                 'BC_Lapse_Rate',
+                 'BC_Emissivity',
+                 'BC_Fixed_Scan_Position']
     # Loop through each variable
     for var in ds.variables:
-
+        # Ignore everything that the user didn't ask for
+        if var not in required_variables:
+            continue
         # Ignore geovals data
         if var in ['air_temperature', 'air_pressure', 'air_pressure_levels',
                    'atmosphere_absorber_01', 'atmosphere_absorber_02', 'atmosphere_absorber_03']:
@@ -155,21 +169,27 @@ def satellite_dataset(ds):
                 out_var = var+pred
                 data_vars[out_var] = (('nobs', 'nchans'), data[:, :, ipred])
 
-        # Deals with how to handle nobs data
+        # Force all variables 2d except lat/lon (optional), or if it inherently has
+        # spectral dependence. Don't touch lat/lon because if they're 2d it makes it
+        # harder to make map. You'd need to do a per channel `accept_where` which
+        # would be cumbersome for hyperspectral, and annoying for any microwave that
+        # isn't AMSU.
+        #
+        # Recommend `gsi_obs_space_reshape_all` for any ancillary data for microwave sensors,
+        # because footprint size is used and calculated on a per channel basis (e.g., Land_Fraction,
+        # Water_Fraction, etc become inherently spectral).
+        elif (force_reshape_all and (var not in lat_lon)) or (var in always_2d):
+            data_vars[var] = (('nobs', 'nchans'), ds[var].data.reshape(iters, nchans))
+
+        # either thin or reshape based on whether or not all repeat values for the first
+        # slice of nchan out of the array. Always use for lat/lon because it will thin
+        # for most sensors except AMSU, which makes it easier to plot a map.
         else:
-            # Check if values repeat over nchans
-            condition = all_equal(ds[var].data[0:nchans])
-
-            # If values are repeating over nchan iterations, keep as nobs
-            if condition:
-                data = ds[var].data[0::nchans]
-                data_vars[var] = (('nobs'), data)
-
-            # Else, reshape to be a 2d array
+            is_1d = (ds[var].isel(nobs=slice(0, nchans)) == ds[var].isel(nobs=0)).all()
+            if (is_1d):
+                data_vars[var] = (('nobs'), ds[var].thin(nobs=nchans).data)
             else:
-                data = np.reshape(ds[var].data, (iters, nchans))
-                data_vars[var] = (('nobs', 'nchans'), data)
-
+                data_vars[var] = (('nobs', 'nchans'), ds[var].data.reshape(iters, nchans))
     # create dataset_config
     new_ds = Dataset(data_vars=data_vars,
                      coords=coords,
@@ -227,7 +247,10 @@ def execute(self, dataset_config, data_collections, timeing):
         # Get the groups to be read
         # -------------------------
         groups = get(dataset_config, self.logger, 'groups')
-
+        if ('gsi_obs_space_reshape_all' in dataset_config.keys()):
+            force_reshape_all = bool(get(dataset_config, self.logger, 'gsi_obs_space_reshape_all'))
+        else:
+            force_reshape_all = False
         # Loop over filenames
         # -------------------
         for filename in filenames:
@@ -251,9 +274,8 @@ def execute(self, dataset_config, data_collections, timeing):
 
                 # Reshape variables if satellite diag
                 if 'nchans' in ds.dims:
-                    ds = satellite_dataset(ds)
+                    ds = satellite_dataset(ds, group_vars, force_reshape_all)
                     ds = subset_channels(ds, channels, self.logger)
-
                 # Adjust variable names if uv
                 if 'variable' in locals():
                     if variable == 'uv':