Documenting WIT ls9

Author: LaurenSchenk1

examples/wit_ls9.conflux.py

@@ -14,6 +14,7 @@
 output_crs = "EPSG:3577"
 resolution = (-30, 30)
 
+# load Water Observations, Landsat 9 and Fractional Cover data
 input_products = {
     "ga_ls_wo_3": ["water"],
     "ga_ls9c_ard_3": [
@@ -52,28 +53,37 @@ def transform(inputs: xr.Dataset) -> xr.Dataset:
 
     tcw = _tcw(ard_ds)
 
+    # divide FC values by 100 to keep them in [0, 1]
     bs = fc_ds.bs / 100
     pv = fc_ds.pv / 100
     npv = fc_ds.npv / 100
 
+    # generate the WIT raster bands
+    # create an empty dataset called 'output_rast' and populate with values from input datasets
     rast_names = ["pv", "npv", "bs", "wet", "water"]
     output_rast = {n: xr.zeros_like(ard_ds) for n in rast_names}
 
     output_rast["bs"] = bs
     output_rast["pv"] = pv
     output_rast["npv"] = npv
 
+    # Mask noncontiguous data, low solar incidence angle, cloud, and water out of the wet category
+    # by disabling those flags 
     mask = (wo_ds.water & 0b01100011) == 0
     # not apply poly_raster cause we will do it before summarise
 
     open_water = wo_ds.water & (1 << 7) > 0
+    
+    # Thresholding 
+    # set wet pixels where not masked and above threshold of -350
     wet = tcw.where(mask) > -350
 
     # TCW
     output_rast["wet"] = wet.astype(float)
     for name in rast_names[:3]:
         output_rast[name].values[wet.values] = 0
 
+    # WO
     output_rast["water"] = open_water.astype(float)
 
     for name in rast_names[0:4]:
@@ -82,13 +92,15 @@ def transform(inputs: xr.Dataset) -> xr.Dataset:
     # save this mask then can do 90% check in summarise
     output_rast["mask"] = (mask).astype(int)
 
+    # masking
     ds_wit = xr.Dataset(output_rast).where(mask)
 
     return ds_wit
 
 
 def summarise(inputs: xr.Dataset) -> xr.Dataset:
 
+    # calculate percentage missing 
     pc_missing = 1 - (np.nansum(inputs.mask.values) / len(inputs.mask.values))
     # inputs = inputs.where(pc_missing < 0.1)