Cif 234 veg water change

city_metrix/layers/__init__.py

@@ -26,3 +26,4 @@
 from .glad_lulc import LandCoverHabitatGlad
 from .glad_lulc import LandCoverHabitatChangeGlad
 from .cams import Cams
+from .vegetation_water_map import VegetationWaterMap

city_metrix/layers/albedo.py

@@ -14,6 +14,8 @@ class Albedo(Layer):
         resampling_method: interpolation method used by Google Earth Engine. Default is 'bilinear'. All options are: ('bilinear', 'bicubic', None).
         threshold: threshold value for filtering the retrieval
     """
+    MAX_CLOUD_PROB = 30
+    S2_ALBEDO_EQN = '((B*Bw)+(G*Gw)+(R*Rw)+(NIR*NIRw)+(SWIR1*SWIR1w)+(SWIR2*SWIR2w))'
 
     def __init__(self, start_date="2021-01-01", end_date="2022-01-01", spatial_resolution:int=10,
                  resampling_method:str='bilinear', threshold=None, **kwargs):
@@ -23,78 +25,70 @@ def __init__(self, start_date="2021-01-01", end_date="2022-01-01", spatial_resol
         self.spatial_resolution = spatial_resolution
         self.resampling_method = resampling_method
         self.threshold = threshold
-
-    def get_data(self, bbox: tuple[float, float, float, float]):
-        S2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
-        S2C = ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY")
-
-        MAX_CLOUD_PROB = 30
-        S2_ALBEDO_EQN = '((B*Bw)+(G*Gw)+(R*Rw)+(NIR*NIRw)+(SWIR1*SWIR1w)+(SWIR2*SWIR2w))'
-
-        ## METHODS
-
-        ## get cloudmasked image collection
-
-        def mask_and_count_clouds(s2wc, geom):
-            s2wc = ee.Image(s2wc)
-            geom = ee.Geometry(geom.geometry())
-            is_cloud = (ee.Image(s2wc.get('cloud_mask'))
-                        .gt(MAX_CLOUD_PROB)
-                        .rename('is_cloud')
-                        )
-
-            nb_cloudy_pixels = is_cloud.reduceRegion(
-                reducer=ee.Reducer.sum().unweighted(),
-                geometry=geom,
-                scale=self.spatial_resolution,
-                maxPixels=1e9
-            )
-            mask = (s2wc
-                    .updateMask(is_cloud.eq(0))
-                    .set('nb_cloudy_pixels',nb_cloudy_pixels.getNumber('is_cloud'))
-                    .divide(10000)
+
+    ## METHODS
+    ## get cloudmasked image collection
+    def mask_and_count_clouds(self, s2wc, geom):
+        s2wc = ee.Image(s2wc)
+        geom = ee.Geometry(geom.geometry())
+        is_cloud = (ee.Image(s2wc.get('cloud_mask'))
+                    .gt(self.MAX_CLOUD_PROB)
+                    .rename('is_cloud')
                     )
 
-            return mask
-
-        def mask_clouds_and_rescale(im):
-            clouds = ee.Image(im.get('cloud_mask')
-                              ).select('probability')
-            mask = im.updateMask(clouds
-                                 .lt(MAX_CLOUD_PROB)
-                                 ).divide(10000)
-
-            return mask
-
-        def get_masked_s2_collection(roi, start, end):
-            criteria = (ee.Filter.And(
-                ee.Filter.date(start, end),
-                ee.Filter.bounds(roi)
-            ))
-            s2 = S2.filter(criteria)  # .select('B2','B3','B4','B8','B11','B12')
-            s2c = S2C.filter(criteria)
-            s2_with_clouds = (
-                ee.Join.saveFirst('cloud_mask')
-                .apply(**{
-                    'primary': ee.ImageCollection(s2),
-                    'secondary': ee.ImageCollection(s2c),
-                    'condition': ee.Filter.equals(**{'leftField': 'system:index', 'rightField': 'system:index'})
-                })
-            )
-
-            def _mcc(im):
-                return mask_and_count_clouds(im, roi)
-                # s2_with_clouds=ee.ImageCollection(s2_with_clouds).map(_mcc)
-
-            # s2_with_clouds=s2_with_clouds.limit(image_limit,'nb_cloudy_pixels')
-            s2_with_clouds = (ee.ImageCollection(s2_with_clouds)
-                              .map(mask_clouds_and_rescale)
-                              )  # .limit(image_limit,'CLOUDY_PIXEL_PERCENTAGE')
-
-            s2_with_clouds_ic = ee.ImageCollection(s2_with_clouds)
-
-            return s2_with_clouds_ic
-
+        nb_cloudy_pixels = is_cloud.reduceRegion(
+            reducer=ee.Reducer.sum().unweighted(),
+            geometry=geom,
+            scale=self.spatial_resolution,
+            maxPixels=1e9
+        )
+        mask = (s2wc
+                .updateMask(is_cloud.eq(0))
+                .set('nb_cloudy_pixels',nb_cloudy_pixels.getNumber('is_cloud'))
+                .divide(10000)
+                )
+
+        return mask
+
+    def mask_clouds_and_rescale(self, im):
+        clouds = ee.Image(im.get('cloud_mask')
+                          ).select('probability')
+        mask = im.updateMask(clouds
+                             .lt(self.MAX_CLOUD_PROB)
+                             ).divide(10000)
+
+        return mask
+
+    def get_masked_s2_collection(self, roi, start, end):
+        criteria = (ee.Filter.And(
+            ee.Filter.date(start, end),
+            ee.Filter.bounds(roi)
+        ))
+        s2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filter(criteria)  # .select('B2','B3','B4','B8','B11','B12')
+        s2c = ee.ImageCollection("COPERNICUS/S2_CLOUD_PROBABILITY").filter(criteria)
+        s2_with_clouds = (
+            ee.Join.saveFirst('cloud_mask')
+            .apply(**{
+                'primary': ee.ImageCollection(s2),
+                'secondary': ee.ImageCollection(s2c),
+                'condition': ee.Filter.equals(**{'leftField': 'system:index', 'rightField': 'system:index'})
+            })
+        )
+
+        def _mcc(im):
+            return self.mask_and_count_clouds(im, roi)
+            # s2_with_clouds=ee.ImageCollection(s2_with_clouds).map(_mcc)
+
+        # s2_with_clouds=s2_with_clouds.limit(image_limit,'nb_cloudy_pixels')
+        s2_with_clouds = (ee.ImageCollection(s2_with_clouds)
+                          .map(self.mask_clouds_and_rescale)
+                          )  # .limit(image_limit,'CLOUDY_PIXEL_PERCENTAGE')
+
+        s2_with_clouds_ic = ee.ImageCollection(s2_with_clouds)
+
+        return s2_with_clouds_ic
+
+    def get_data(self, bbox):
         # calculate albedo for images
         # weights derived from
         # S. Bonafoni and A. Sekertekin, "Albedo Retrieval From Sentinel-2 by New Narrow-to-Broadband Conversion Coefficients," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 9, pp. 1618-1622, Sept. 2020, doi: 10.1109/LGRS.2020.2967085.
@@ -114,12 +108,12 @@ def calc_s2_albedo(image):
                 'SWIR2': image.select('B12')
             }
 
-            albedo = image.expression(S2_ALBEDO_EQN, config).double().rename('albedo')
+            albedo = image.expression(self.S2_ALBEDO_EQN, config).double().rename('albedo')
 
             return albedo
 
-        ## S2 MOSAIC AND ALBEDO
-        dataset = get_masked_s2_collection(ee.Geometry.BBox(*bbox), self.start_date, self.end_date)
+        # S2 MOSAIC AND ALBEDO
+        dataset = self.get_masked_s2_collection(ee.Geometry.BBox(*bbox), self.start_date, self.end_date)
         s2_albedo = dataset.map(calc_s2_albedo)
 
         albedo_mean = (s2_albedo