def apply_mask_cloud(image_coll, collection_name, cloudy_pix_flag):
"""
Different input_collections need different steps to be taken to handle
cloudy image. The first step is to reject images that more than X%
cloudy pixels (here X=5). The next step is to mask cloudy pixels. This
will hopefully mean that when we take the median of the ImageCollection,
we ignore cloudy pixels.
Parameters
----------
image_coll : ee.ImageCollection
The ImageCollection of images from which we want to remove cloud.
collection_name : str
Name of the collection so that we can apply collection specific
masking.
cloud_pix_flag : str
Name of the flag which details the fraction of cloudy pixels in each
image.
Returns
----------
image_coll
Image collection with very cloudy images removed, and masked images
containing a tolerable amount of cloud.
"""
# construct cloud mask if availible
if collection_name == 'COPERNICUS/S2':
mask_func = cloud_mask.sentinel2()
elif collection_name == 'LANDSAT/LC08/C01/T1_SR':
mask_func = cloud_mask.landsat8SRPixelQA()
elif (collection_name == 'LANDSAT/LE07/C01/T1_SR'
or collection_name == 'LANDSAT/LT05/C01/T1_SR'
or collection_name == 'LANDSAT/LT04/C01/T1_SR'):
mask_func = cloud_mask.landsat457SRPixelQA()
else:
print("No cloud mask logic defined for input collection {}"\
.format(collection_name))
return image_coll
# images with more than this percent of cloud pixels are removed
cloud_pix_frac = 50
# remove images that have more than `cloud_pix_frac`% cloudy pixels
if cloudy_pix_flag != 'None':
image_coll = image_coll.filter(
ee.Filter.lt(cloudy_pix_flag, cloud_pix_frac))
# apply per pixel cloud mask
image_coll = image_coll.map(mask_func)
return image_coll
def test_snow():
masked = cloud_mask.landsat8SRPixelQA(['snow'])(image)
vals = getValue(masked, p_snow, 30)
assert vals.get("B1").getInfo() == None