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Update clouds.py with SR and TOA specific masking fxns
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@mjevans26
mjevans26 committed Jul 1, 2020
1 parent 25a3f9e commit e5bc168
Showing 1 changed file with 69 additions and 33 deletions.
102 changes: 69 additions & 33 deletions utils/clouds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import ee

# Initialize Earth Engine
ee.Initialize()
JRC = ee.ImageCollection("JRC/GSW1_1/YearlyHistory")

def sentinel2toa(img):
"""
Expand All @@ -29,23 +29,6 @@ def rescale(img, exp, thresholds):
#return img.subtract(thresholds[0]).divide(thresholds[1]-thresholds[0])
return img.expression(exp, {'img': img}).subtract(thresholds[0]).divide(thresholds[1] - thresholds[0])

def maskS2SR(img):
"""
Apply built in masks to Sentinel-2 surface reflectance imagery
Parameters:
img (ee.Image): Sentinel-2 level 2A surface reflectange image
Returns:
ee.Image: masked image
"""
scored = basicQA(img)
maskBand = img.select('SCL')
cloudMask = maskBand.neq(8).And(maskBand.neq(9))
waterMask = maskBand.neq(6)
cirrusMask = maskBand.neq(10)
snowMask = maskBand.neq(11)
darkMask = maskBand.neq(2).And(maskBand.neq(3))
return scored.updateMask(cloudMask.And(waterMask).And(cirrusMask).And(snowMask).And(darkMask))

def waterScore(img):
"""
Calculate a water likelihood score [0, 1]
Expand All @@ -56,7 +39,6 @@ def waterScore(img):
Returns:
ee.Image: image with single ['waterscore'] band
"""
print('waterScore:', img)
img = sentinel2toa(img)
# Compute several indicators of water and take the minimum of them.
score = ee.Image(1.0)
Expand Down Expand Up @@ -99,14 +81,18 @@ def basicQA(img):
Returns:
ee.Image: original image masked for clouds and cirrus
"""
#print('basicQA:', img)
qa = img.select('QA60').int16()
# print('qa:', type(qa))
# qa = img.select(['QA60']).int16()
#print('qa:', qa.getInfo())
# Bits 10 and 11 are clouds and cirrus, respectively.
cloudBitMask = 1024 # math.pow(2, 10)
cirrusBitMask = 2048 #math.pow(2, 11)
# Both flags should be set to zero, indicating clear conditions.
#mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))
dated = img.updateMask(mask).divide(10000).copyProperties(img)
dated = img.updateMask(mask)
#dated = img.addBands(img.metadata('system:time_start', 'date')).updateMask(mask)
return dated

Expand Down Expand Up @@ -154,35 +140,85 @@ def sentinelCloudScore(img):
Returns:
ee.Image: original image with added ['cloudScore'] band
"""
#print('sentinelCloudScore:', img)
im = sentinel2toa(img)
# Compute several indicators of cloudyness and take the minimum of them.
# Compute several indicators of cloudyness and take the minimum of them.
score = ee.Image(1)

# Clouds are reasonably bright in the blue and cirrus bands.
#score = score.min(rescale(im.select(['B2']), [0.1, 0.5]))
# Clouds are reasonably bright in the blue and cirrus bands.
#score = score.min(rescale(im.select(['B2']), [0.1, 0.5]))
score = score.min(rescale(im, 'img.B2', [0.1, 0.5]))
#score = score.min(rescale(im.select(['B1']), [0.1, 0.3]))
#score = score.min(rescale(im.select(['B1']), [0.1, 0.3]))
score = score.min(rescale(im, 'img.B1', [0.1, 0.3]))
#score = score.min(rescale(im.select(['B1']).add(im.select(['B10'])), [0.15, 0.2]))
#score = score.min(rescale(im.select(['B1']).add(im.select(['B10'])), [0.15, 0.2]))
score = score.min(rescale(im, 'img.B1 + img.B10', [0.15, 0.2]))

# Clouds are reasonably bright in all visible bands.
#score = score.min(rescale(im.select('B4').add(im.select('B3')).add(im.select('B2')), [0.2, 0.8]))
# Clouds are reasonably bright in all visible bands.
#score = score.min(rescale(im.select('B4').add(im.select('B3')).add(im.select('B2')), [0.2, 0.8]))
score = score.min(rescale(im, 'img.B4 + img.B3 + img.B2', [0.2, 0.8]))

# Clouds are moist
# Clouds are moist
ndmi = im.normalizedDifference(['B8','B11'])
#score=score.min(rescale(ndmi, [-0.1, 0.1]))
#score=score.min(rescale(ndmi, [-0.1, 0.1]))
score=score.min(rescale(ndmi, 'img', [-0.1, 0.1]))

# However, clouds are not snow.
# However, clouds are not snow.
ndsi = im.normalizedDifference(['B3', 'B11'])
#score=score.min(rescale(ndsi, [0.8, 0.6]))
#score=score.min(rescale(ndsi, [0.8, 0.6]))
score=score.min(rescale(ndsi, 'img', [0.8, 0.6]))

score = score.multiply(100).byte()
print('score:', type(score))
#print('score:', type(score))

return img.addBands(score.rename(['cloudScore']))

def mask(img):
date = img.date()
year = date.get('year')
month = date.get('month')
cdi = ee.Algorithms.Sentinel2.CDI(img)
scored = basicQA(img)
clouds = sentinelCloudScore(scored).lte(15).Or(cdi.gte(-0.2))
water = waterScore(img).select('waterScore').lte(0.25)
jrc = ee.Image(JRC.filterMetadata('month', 'equals', month).filterMetadata('year', 'equals', year).first())
waterMask = jrc.focal_max(1, 'square', 'pixels').neq(2).And(water)
shadowMask = img.select('B11').gt(900)
return scored.updateMask(clouds.And(shadowMask).And(waterMask))

def maskSR(img):
"""
Apply built in masks to Sentinel-2 surface reflectance imagery
Parameters:
img (ee.Image): Sentinel-2 level 2A surface reflectange image
Returns:
ee.Image: masked image
"""
jrc = ee.Image('JRC/GSW1_1/YearlyHistory/2018')
scored = basicQA(img);
maskBand = img.select('SCL')
cloudMask = maskBand.neq(8).And(maskBand.neq(9))
waterMask = maskBand.neq(6).where(jrc.gte(2), 0)
cirrusMask = maskBand.neq(10)
snowMask = maskBand.neq(11)
darkMask = maskBand.neq(2).And(maskBand.neq(3))
return scored.updateMask(cloudMask.And(waterMask).And(cirrusMask).And(snowMask).And(darkMask))

def maskTOA(img):
"""
Mask Sentinel-2 1C top of atmosphere imagery for clouds, water, shadow
Parameters:
img (ee.Image): Sentinel-2 level 1C image
Returns:
ee.Image: masked image
"""
date = img.date()
year = date.get('year')
#month = date.get('month')
#cdi = ee.Algorithms.Sentinel2.CDI(img)
scored = basicQA(img)
cloudMask = sentinelCloudScore(scored).select('cloudScore').lte(15)#.Or(cdi.gte(-0.2))
water = waterScore(img).select('waterScore').lte(0.25)
jrc = ee.Image(JRC.filterMetadata('year', 'equals', year).first())
watermask = water.where(jrc.gte(2), 0)
shadowMask = img.select('B11').gt(900)
return scored.updateMask(cloudMask.And(shadowMask).And(watermask))

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