notebook S2 preprocessing

docs/source/sentinel_2/s2_func_prepro.py

@@ -140,7 +140,7 @@ def clip_imgs(list_im_to_clip, clipped_path, roi_gdf):
 
 
 
-def apply_SCL(list_im_ROI, clipped_path, masked_path, nodata_val):
+def apply_SCL(list_im_ROI, clipped_path, masked_path, nodata_val,values_to_mask = [0,1,2,3,8,9,10,11]):
     for im_file in list_im_ROI:
         
         # Get date of image
@@ -168,20 +168,12 @@ def apply_SCL(list_im_ROI, clipped_path, masked_path, nodata_val):
             #show(SCL, cmap='Set3')
 
             SCL = SCL.astype(float)
-
-            SCL[SCL == 0] = np.nan    # No data
-            SCL[SCL == 1] = np.nan    # Saturated or defective
-            SCL[SCL == 2] = np.nan    # Dark area pixels
-            SCL[SCL == 3] = np.nan    # Cloud shadows
-            SCL[SCL == 4] = 1         # Vegetation
-            SCL[SCL == 5] = 1         # Not vegetated
-            SCL[SCL == 6] = 1         # Water
-            SCL[SCL == 7] = 1         # Unclassified
-            SCL[SCL == 8] = np.nan    # Cloud medium probability
-            SCL[SCL == 9] = np.nan    # Cloud high probability
-            SCL[SCL == 10] = np.nan   # Thin cirrus
-            SCL[SCL == 11] = np.nan   # Snow
-
+            
+            for i in values_to_mask:
+                SCL[SCL == i] = np.nan
+            
+            SCL[~np.isnan(SCL)] = 1
+            
             # Open file
             src = rasterio.open(im_file, "r")