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sentineLandsat.py
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sentineLandsat.py
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# Sentinel-2 package
from paramsTemplate import *
import ee
from Py6S import *
import math
import datetime
import os, sys
from utils import *
import sun_angles
import view_angles
import time
class env(object):
def __init__(self):
"""Initialize the environment."""
# Initialize the Earth Engine object, using the authentication credentials.
ee.Initialize()
self.dem = ee.Image("JAXA/ALOS/AW3D30_V1_1").select(["AVE"])
self.epsg = "EPSG:4326"
##########################################
# variable for the landsat data request #
##########################################
self.metadataCloudCoverMax = 80;
##########################################
# Export variables #
##########################################
#self.assetId ="projects/Sacha/PreprocessedData/L8_Biweekly_V4/"
self.assetId ="projects/Sacha/PreprocessedData/S2_Annual_V3/"
self.name = "S2_ECUADOR_ANNUAL_MEDIAN_CLDMX80_2018_000365"
self.exportScale = 20
##########################################
# variable for the shadowMask algorithm #
##########################################
# zScoreThresh: Threshold for cloud shadow masking- lower number masks out
# less. Between -0.8 and -1.2 generally works well
self.zScoreThresh = -0.9
# shadowSumThresh: Sum of IR bands to include as shadows within TDOM and the
# shadow shift method (lower number masks out less)
self.shadowSumThresh = 0.30;
# contractPixels: The radius of the number of pixels to contract (negative buffer) clouds and cloud shadows by. Intended to eliminate smaller cloud
# patches that are likely errors (1.5 results in a -1 pixel buffer)(0.5 results in a -0 pixel buffer)
# (1.5 or 2.5 generally is sufficient)
self.contractPixels = 1.5;
# dilatePixels: The radius of the number of pixels to dilate (buffer) clouds
# and cloud shadows by. Intended to include edges of clouds/cloud shadows
# that are often missed (1.5 results in a 1 pixel buffer)(0.5 results in a 0 pixel buffer)
# (2.5 or 3.5 generally is sufficient)
self.dilatePixels = 3.25;
##########################################
# variable for cloudScore algorithm #
##########################################
# 9. Cloud and cloud shadow masking parameters.
# If cloudScoreTDOM is chosen
# cloudScoreThresh: If using the cloudScoreTDOMShift method-Threshold for cloud
# masking (lower number masks more clouds. Between 10 and 30 generally works best)
self.cloudScoreThresh = 30;
# Percentile of cloud score to pull from time series to represent a minimum for
# the cloud score over time for a given pixel. Reduces commission errors over
# cool bright surfaces. Generally between 5 and 10 works well. 0 generally is a bit noisy
self.cloudScorePctl = 8
##########################################
# variable for terrain algorithm #
##########################################
self.terrainScale = 600
##########################################
# variable band selection #
##########################################
self.divideBandsLandsat = ee.List(['blue','green','red','nir','swir1','swir2'])
self.bandNamesLandsat = ee.List(['blue','green','red','nir','swir1','thermal','swir2','qa'])
self.sensorBandDictLandsatSR = ee.Dictionary({'L8' : ee.List([1,2,3,4,5,9,6,10]),\
'L7' : ee.List([0,1,2,3,4,5,8,9])})
self.s2BandsIn = ee.List(['QA60','B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B10','B11','B12'])
self.s2BandsOut = ee.List(['QA60','cb','blue','green','red','re1','re2','re3','nir','nir2','waterVapor','cirrus','swir1','swir2'])
self.divideBands = ee.List(['blue','green','red','re1','re2','re3','nir','nir2','cb','cirrus','swir1','swir2','waterVapor'])
self.medianIncludeBands = ee.List(['blue','green','red','re1','re2','re3','nir','nir2','cb','cirrus','swir1','swir2','waterVapor'])
##########################################
# enable / disable modules #
##########################################
self.cloudMask = True
self.QAcloudMask = True
self.shadowMask = True
self.brdfCorrect = False
self.terrainCorrection = False
class Landsat():
def __init__(self):
"""Initialize the Surfrace Reflectance app."""
# get the environment
self.env = env()
def main(self,studyArea,startDate,endDate,startDay,endDay,week,regionName):
self.env.startDate = startDate
self.env.endDate = endDate
self.env.startDoy = startDay
self.env.endDoy = endDay
self.env.regionName = regionName
self.studyArea = studyArea
landsat8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA').filterDate(self.env.startDate,self.env.endDate).filterBounds(studyArea)
landsat8 = landsat8.filterMetadata('CLOUD_COVER','less_than',self.env.metadataCloudCoverMax)
landsat8 = landsat8.select(self.env.sensorBandDictLandsatSR.get('L8'),self.env.bandNamesLandsat)
landsat7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA').filterDate(self.env.startDate,self.env.endDate).filterBounds(studyArea)
landsat7 = landsat7.filterMetadata('CLOUD_COVER','less_than',self.env.metadataCloudCoverMax)
landsat7 = landsat7.select(self.env.sensorBandDictLandsatSR.get('L7'),self.env.bandNamesLandsat)
landsat7 = landsat7.map(self.HarmonizeLandsat7)
landsat8 = landsat8.map(self.HarmonizeLandsat8)
#landsat = landsat7.merge(landsat8)
landsat = landsat8
if landsat.size().getInfo() > 0:
# mask clouds using the QA band
#if self.env.maskSR == True:
#print "removing clouds"
#landsat = landsat.map(self.CloudMaskSRL8)
# mask clouds using cloud mask function
if self.env.shadowMask == True:
#print "shadow masking"
self.fullCollection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA').filterBounds(studyArea).filter(ee.Filter.lt("CLOUD_COVER",30))\
.select(self.env.sensorBandDictLandsatSR.get('L8'),self.env.bandNamesLandsat)
l7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA').filterBounds(studyArea).filter(ee.Filter.lt("CLOUD_COVER",30))\
.select(self.env.sensorBandDictLandsatSR.get('L7'),self.env.bandNamesLandsat)
self.fullCollection = self.fullCollection.merge(l7)
landsat = self.maskShadows(landsat)
#landsat = landsat.map(self.scaleLandsat).map(self.addDateYear)
landsat = landsat.map(self.addDateYear)
# mask clouds using cloud mask function
if self.env.cloudMask == True:
#print "removing some more clouds"
landsat = landsat.map(self.maskClouds)
if self.env.brdfCorrect == True:
landsat = landsat.map(self.brdf)
if self.env.terrainCorrection == True:
landsat = ee.ImageCollection(landsat.map(self.terrain))
return landsat
def addDateYear(self,img):
#add a date and year band
date = ee.Date(img.get("system:time_start"))
day = date.getRelative('day','year').add(1);
yr = date.get('year');
mk = img.mask().reduce(ee.Reducer.min());
img = img.addBands(ee.Image.constant(day).mask(mk).uint16().rename(['date']));
img = img.addBands(ee.Image.constant(yr).mask(mk).uint16().rename(['year']));
return img;
def CloudMaskSRL8(self,img):
"""apply cf-mask Landsat"""
QA = img.select("qa")
cloud = QA.bitwiseAnd(4).neq(0);
return img.updateMask(cloud.Not()).copyProperties(img)
def scaleLandsat(self,img):
"""Landast is scaled by factor 0.0001 """
thermal = img.select(ee.List(['thermal'])).multiply(0.1)
scaled = ee.Image(img).select(self.env.divideBandsLandsat).multiply(ee.Number(0.0001))
return img.select(['TDOMMask']).addBands(scaled).addBands(thermal)
def reScaleLandsat(self,img):
"""Landast is scaled by factor 0.0001 """
noScaleBands = ee.List(['date','year','TDOMMask','cloudMask','count'])
noScale = ee.Image(img).select(noScaleBands)
thermalBand = ee.List(['thermal'])
thermal = ee.Image(img).select(thermalBand).multiply(10)
otherBands = ee.Image(img).bandNames().removeAll(thermalBand).removeAll(noScaleBands)
scaled = ee.Image(img).select(otherBands).divide(0.0001)
image = ee.Image(scaled.addBands([thermal,noScale])).int16()
return image.copyProperties(img)
def maskClouds(self,img):
"""
Computes spectral indices of cloudyness and take the minimum of them.
Each spectral index is fairly lenient because the group minimum
is a somewhat stringent comparison policy. side note -> this seems like a job for machine learning :)
originally written by Matt Hancher for Landsat imageryadapted to Sentinel by Chris Hewig and Ian Housman
"""
score = ee.Image(1.0);
# Clouds are reasonably bright in the blue band.
blue_rescale = img.select('blue').subtract(ee.Number(0.1)).divide(ee.Number(0.3).subtract(ee.Number(0.1)))
score = score.min(blue_rescale);
# Clouds are reasonably bright in all visible bands.
visible = img.select('red').add(img.select('green')).add(img.select('blue'))
visible_rescale = visible.subtract(ee.Number(0.2)).divide(ee.Number(0.8).subtract(ee.Number(0.2)))
score = score.min(visible_rescale);
# Clouds are reasonably bright in all infrared bands.
infrared = img.select('nir').add(img.select('swir1')).add(img.select('swir2'))
infrared_rescale = infrared.subtract(ee.Number(0.3)).divide(ee.Number(0.8).subtract(ee.Number(0.3)))
score = score.min(infrared_rescale);
# Clouds are reasonably cool in temperature.
temp_rescale = img.select('thermal').subtract(ee.Number(300)).divide(ee.Number(290).subtract(ee.Number(300)))
score = score.min(temp_rescale);
# However, clouds are not snow.
ndsi = img.normalizedDifference(['green', 'swir1']);
ndsi_rescale = ndsi.subtract(ee.Number(0.8)).divide(ee.Number(0.6).subtract(ee.Number(0.8)))
score = score.min(ndsi_rescale).multiply(100).byte();
mask = score.lt(self.env.cloudScoreThresh).rename(['cloudMask']);
img = img.updateMask(mask).addBands([mask]);
return img;
def maskShadows(self,collection):
def TDOM(image):
zScore = image.select(shadowSumBands).subtract(irMean).divide(irStdDev)
irSum = image.select(shadowSumBands).reduce(ee.Reducer.sum())
TDOMMask = zScore.lt(self.env.zScoreThresh).reduce(ee.Reducer.sum()).eq(2)\
.And(irSum.lt(self.env.shadowSumThresh)).Not()
TDOMMask = TDOMMask.focal_min(self.env.contractPixels).focal_max(self.env.dilatePixels).rename(['TDOMMask'])
image = image.addBands([TDOMMask])
return image.updateMask(TDOMMask)
shadowSumBands = ['nir','swir1']
# Get some pixel-wise stats for the time series
irStdDev = self.fullCollection.select(shadowSumBands).reduce(ee.Reducer.stdDev())
irMean = self.fullCollection.select(shadowSumBands).reduce(ee.Reducer.mean())
# Mask out dark dark outliers
collection_tdom = collection.map(TDOM)
return collection_tdom
def terrain(self,img):
degree2radian = 0.01745;
otherBands = img.select(['thermal','date','year','TDOMMask','cloudMask'])
def topoCorr_IC(img):
dem = ee.Image("USGS/SRTMGL1_003")
# Extract image metadata about solar position
SZ_rad = ee.Image.constant(ee.Number(img.get('SOLAR_ZENITH_ANGLE'))).multiply(degree2radian).clip(img.geometry().buffer(10000));
SA_rad = ee.Image.constant(ee.Number(img.get('SOLAR_AZIMUTH_ANGLE'))).multiply(degree2radian).clip(img.geometry().buffer(10000));
# Creat terrain layers
slp = ee.Terrain.slope(dem).clip(img.geometry().buffer(10000));
slp_rad = ee.Terrain.slope(dem).multiply(degree2radian).clip(img.geometry().buffer(10000));
asp_rad = ee.Terrain.aspect(dem).multiply(degree2radian).clip(img.geometry().buffer(10000));
# Calculate the Illumination Condition (IC)
# slope part of the illumination condition
cosZ = SZ_rad.cos();
cosS = slp_rad.cos();
slope_illumination = cosS.expression("cosZ * cosS", \
{'cosZ': cosZ, 'cosS': cosS.select('slope')});
# aspect part of the illumination condition
sinZ = SZ_rad.sin();
sinS = slp_rad.sin();
cosAziDiff = (SA_rad.subtract(asp_rad)).cos();
aspect_illumination = sinZ.expression("sinZ * sinS * cosAziDiff", \
{'sinZ': sinZ, \
'sinS': sinS, \
'cosAziDiff': cosAziDiff});
# full illumination condition (IC)
ic = slope_illumination.add(aspect_illumination);
# Add IC to original image
img_plus_ic = ee.Image(img.addBands(ic.rename(['IC'])).addBands(cosZ.rename(['cosZ'])).addBands(cosS.rename(['cosS'])).addBands(slp.rename(['slope'])));
return ee.Image(img_plus_ic);
def topoCorr_SCSc(img):
img_plus_ic = img;
mask1 = img_plus_ic.select('nir').gt(-0.1);
mask2 = img_plus_ic.select('slope').gte(5) \
.And(img_plus_ic.select('IC').gte(0)) \
.And(img_plus_ic.select('nir').gt(-0.1));
img_plus_ic_mask2 = ee.Image(img_plus_ic.updateMask(mask2));
bandList = ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']; # Specify Bands to topographically correct
def applyBands(image):
blue = apply_SCSccorr('blue').select(['blue'])
green = apply_SCSccorr('green').select(['green'])
red = apply_SCSccorr('red').select(['red'])
nir = apply_SCSccorr('nir').select(['nir'])
swir1 = apply_SCSccorr('swir1').select(['swir1'])
swir2 = apply_SCSccorr('swir2').select(['swir2'])
return replace_bands(image, [blue, green, red, nir, swir1, swir2])
def apply_SCSccorr(band):
method = 'SCSc';
out = ee.Image(1).addBands(img_plus_ic_mask2.select('IC', band)).reduceRegion(reducer= ee.Reducer.linearRegression(2,1), \
geometry= ee.Geometry(img.geometry().buffer(-5000)), \
scale= self.env.terrainScale, \
bestEffort =True,
maxPixels=1e10)
#out_a = ee.Number(out.get('scale'));
#out_b = ee.Number(out.get('offset'));
#out_c = ee.Number(out.get('offset')).divide(ee.Number(out.get('scale')));
fit = out.combine({"coefficients": ee.Array([[1],[1]])}, False);
#Get the coefficients as a nested list,
#cast it to an array, and get just the selected column
out_a = (ee.Array(fit.get('coefficients')).get([0,0]));
out_b = (ee.Array(fit.get('coefficients')).get([1,0]));
out_c = out_a.divide(out_b)
# apply the SCSc correction
SCSc_output = img_plus_ic_mask2.expression("((image * (cosB * cosZ + cvalue)) / (ic + cvalue))", {
'image': img_plus_ic_mask2.select([band]),
'ic': img_plus_ic_mask2.select('IC'),
'cosB': img_plus_ic_mask2.select('cosS'),
'cosZ': img_plus_ic_mask2.select('cosZ'),
'cvalue': out_c });
return ee.Image(SCSc_output);
#img_SCSccorr = ee.Image([apply_SCSccorr(band) for band in bandList]).addBands(img_plus_ic.select('IC'));
img_SCSccorr = applyBands(img).select(bandList).addBands(img_plus_ic.select('IC'))
bandList_IC = ee.List([bandList, 'IC']).flatten();
img_SCSccorr = img_SCSccorr.unmask(img_plus_ic.select(bandList_IC)).select(bandList);
return img_SCSccorr.unmask(img_plus_ic.select(bandList))
img = topoCorr_IC(img)
img = topoCorr_SCSc(img)
return img.addBands(otherBands)
def defringe(self,img):
# threshold for defringing landsat5 and 7
fringeCountThreshold = 279
k = ee.Kernel.fixed(41, 41,
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]);
m = ee.Image(img).mask().reduce(ee.Reducer.min())
sum = m.reduceNeighborhood(ee.Reducer.sum(), k, 'kernel')
mask = sum.gte(fringeCountThreshold)
return img.updateMask(mask)
def brdf(self,img):
import sun_angles
import view_angles
def _apply(image, kvol, kvol0):
blue = _correct_band(image, 'blue', kvol, kvol0, f_iso=0.0774, f_geo=0.0079, f_vol=0.0372)
green = _correct_band(image, 'green', kvol, kvol0, f_iso=0.1306, f_geo=0.0178, f_vol=0.0580)
red = _correct_band(image, 'red', kvol, kvol0, f_iso=0.1690, f_geo=0.0227, f_vol=0.0574)
nir = _correct_band(image, 'nir', kvol, kvol0, f_iso=0.3093, f_geo=0.0330, f_vol=0.1535)
swir1 = _correct_band(image, 'swir1', kvol, kvol0, f_iso=0.3430, f_geo=0.0453, f_vol=0.1154)
swir2 = _correct_band(image, 'swir2', kvol, kvol0, f_iso=0.2658, f_geo=0.0387, f_vol=0.0639)
return replace_bands(image, [blue, green, red, nir, swir1, swir2])
def _correct_band(image, band_name, kvol, kvol0, f_iso, f_geo, f_vol):
"""fiso + fvol * kvol + fgeo * kgeo"""
iso = ee.Image(f_iso)
geo = ee.Image(f_geo)
vol = ee.Image(f_vol)
pred = vol.multiply(kvol).add(geo.multiply(kvol)).add(iso).rename(['pred'])
pred0 = vol.multiply(kvol0).add(geo.multiply(kvol0)).add(iso).rename(['pred0'])
cfac = pred0.divide(pred).rename(['cfac'])
corr = image.select(band_name).multiply(cfac).rename([band_name])
return corr
def _kvol(sunAz, sunZen, viewAz, viewZen):
"""Calculate kvol kernel.
From Lucht et al. 2000
Phase angle = cos(solar zenith) cos(view zenith) + sin(solar zenith) sin(view zenith) cos(relative azimuth)"""
relative_azimuth = sunAz.subtract(viewAz).rename(['relAz'])
pa1 = viewZen.cos() \
.multiply(sunZen.cos())
pa2 = viewZen.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle1 = pa1.add(pa2)
phase_angle = phase_angle1.acos()
p1 = ee.Image(PI().divide(2)).subtract(phase_angle)
p2 = p1.multiply(phase_angle1)
p3 = p2.add(phase_angle.sin())
p4 = sunZen.cos().add(viewZen.cos())
p5 = ee.Image(PI().divide(4))
kvol = p3.divide(p4).subtract(p5).rename(['kvol'])
viewZen0 = ee.Image(0)
pa10 = viewZen0.cos() \
.multiply(sunZen.cos())
pa20 = viewZen0.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle10 = pa10.add(pa20)
phase_angle0 = phase_angle10.acos()
p10 = ee.Image(PI().divide(2)).subtract(phase_angle0)
p20 = p10.multiply(phase_angle10)
p30 = p20.add(phase_angle0.sin())
p40 = sunZen.cos().add(viewZen0.cos())
p50 = ee.Image(PI().divide(4))
kvol0 = p30.divide(p40).subtract(p50).rename(['kvol0'])
return (kvol, kvol0)
date = img.date()
footprint = determine_footprint(img)
(sunAz, sunZen) = sun_angles.create(date, footprint)
(viewAz, viewZen) = view_angles.create(footprint)
(kvol, kvol0) = _kvol(sunAz, sunZen, viewAz, viewZen)
return _apply(img, kvol.multiply(PI()), kvol0.multiply(PI()))
def medoidMosaic(self,collection):
""" medoid composite with equal weight among indices """
nImages = ee.ImageCollection(collection).select([0]).count().rename('count')
bandNames = ee.Image(collection.first()).bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = collection.select(otherBands).reduce(ee.Reducer.mean()).rename(otherBands);
collection = collection.select(self.env.divideBands)
bandNumbers = ee.List.sequence(1,self.env.divideBands.length());
median = ee.ImageCollection(collection).median()
def subtractmedian(img):
diff = ee.Image(img).subtract(median).pow(ee.Image.constant(2));
return diff.reduce('sum').addBands(img);
medoid = collection.map(subtractmedian)
medoid = ee.ImageCollection(medoid).reduce(ee.Reducer.min(self.env.divideBands.length().add(1))).select(bandNumbers,self.env.divideBands);
return medoid.addBands(others).addBands(nImages);
def medianMosaic(self,collection):
""" median composite """
median = collection.select(medianIncludeBands).median();
othersBands = bandNames.removeAll(medianIncludeBands);
others = collection.select(otherBands).mean();
return median.addBands(others)
def setMetaData(self,img):
img = ee.Image(img).set({'regionName': str(self.env.regionName),
'system:time_start':ee.Date(self.env.startDate).millis(),
'startDOY':str(self.env.startDoy),
'endDOY':str(self.env.endDoy),
'assetId':str(self.env.assetId),
'compositingMethod':'medoid',
'toaOrSR':'SR',
'epsg':str(self.env.epsg),
'exportScale':str(self.env.exportScale),
'shadowSumThresh':str(self.env.shadowSumThresh),
'maskSR':str(self.env.maskSR),
'cloudMask':str(self.env.cloudMask),
'hazeMask':str(self.env.hazeMask),
'shadowMask':str(self.env.shadowMask),
'brdfCorrect':str(self.env.brdfCorrect),
'terrainCorrection':str(self.env.terrainCorrection),
'contractPixels':str(self.env.contractPixels),
'dilatePixels':str(self.env.dilatePixels),
'zScoreThresh':str(self.env.zScoreThresh),
'metadataCloudCoverMax':str(self.env.metadataCloudCoverMax),
'cloudScorePctl':str(self.env.cloudScorePctl),
'hazeThresh':str(self.env.hazeThresh),
'terrainScale':str(self.env.terrainScale)})
return img
def HarmonizeLandsat7(self,img):
t = img.get("system:time_start")
bandNames = ee.List(['blue','green','red','nir','swir1','swir2']);
slopes = [1.10601,0.99091,1.05681,1.0045,1.03611,1.04011]
intercepts = [-0.0139,0.00411,-0.0024,-0.0076,0.00411,0.00861]
otherBands = ee.Image(img).bandNames().removeAll(bandNames)
others = img.select(otherBands)
img = ee.Image(img).select(bandNames).multiply(slopes).add(intercepts).float();
return img.addBands(others).copyProperties(img).set("system:time_start",t)
def HarmonizeLandsat8(self,img):
t = img.get("system:time_start")
bandNames = ee.List(['blue','green','red','nir','swir1','swir2']);
slopes = [1.0946,1.0043,1.0524,0.8954,1.0049,1.0002];
intercepts = [-0.0107,0.0026,-0.0015,0.0033,0.0065,0.0046];
otherBands = ee.Image(img).bandNames().removeAll(bandNames)
others = img.select(otherBands)
img = ee.Image(img).select(bandNames).multiply(slopes).add(intercepts).float();
return img.addBands(others).copyProperties(img).set("system:time_start",t)
class sentinel2():
def __init__(self):
"""Initialize the Surfrace Reflectance app."""
# get the environment
self.env = env()
def main(self,studyArea,startDate,endDate,startDay,endDay,week,regionName):
self.env.regionName = regionName
self.env.startDate = startDate
self.env.endDate = endDate
self.env.startDoy = startDay
self.env.endDoy = endDay
s2 = self.getSentinel2(startDate,endDate,studyArea);
print(s2.size().getInfo())
if s2.size().getInfo() > 0:
s2 = s2.map(self.scaleS2)
# masking the shadows
if self.env.shadowMask == True:
s2 = self.maskShadows(s2,studyArea)
# self.collectionMeta = s2.getInfo()['features']
s2 = s2.select(self.env.s2BandsIn,self.env.s2BandsOut).map(self.addDateYear)
if self.env.QAcloudMask == True:
s2 = s2.map(self.QAMaskCloud)
if self.env.cloudMask == True:
s2 = s2.map(self.sentinelCloudScore)
s2 = self.cloudMasking(s2)
if self.env.brdfCorrect == True:
s2 = s2.map(self.brdf)
if self.env.terrainCorrection == True:
s2 = s2.map(self.getTopo)
corrected = s2.filter(ee.Filter.gt("slope",20))
notCorrected = s2.filter(ee.Filter.lt("slope",20))
s2 = corrected.map(self.terrain).merge(notCorrected)
return s2
def getSentinel2(self,start,end,studyArea):
s2s = ee.ImageCollection('COPERNICUS/S2').filterDate(start,end) \
.filterBounds(studyArea) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',self.env.metadataCloudCoverMax)) \
.filter(ee.Filter.lt('CLOUD_COVERAGE_ASSESSMENT',self.env.metadataCloudCoverMax))\
return s2s
def addDateYear(self,img):
#add a date and year band
date = ee.Date(img.get("system:time_start"))
day = date.getRelative('day','year').add(1);
yr = date.get('year');
mk = img.mask().reduce(ee.Reducer.min());
img = img.addBands(ee.Image.constant(day).mask(mk).uint16().rename('date'));
img = img.addBands(ee.Image.constant(yr).mask(mk).uint16().rename('year'));
return img;
def maskShadows(self,collection,studyArea):
def TDOM(image):
zScore = image.select(shadowSumBands).subtract(irMean).divide(irStdDev)
irSum = image.select(shadowSumBands).reduce(ee.Reducer.sum())
TDOMMask = zScore.lt(self.env.zScoreThresh).reduce(ee.Reducer.sum()).eq(2)\
.And(irSum.lt(self.env.shadowSumThresh)).Not()
TDOMMask = TDOMMask.focal_min(self.env.dilatePixels)
return image.addBands(TDOMMask.rename(['TDOMMask']))
def mask(image):
outimg = image.updateMask(image.select(['TDOMMask']))
return outimg
shadowSumBands = ['B8','B11']
allCollection = ee.ImageCollection('COPERNICUS/S2').filterBounds(studyArea).filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE",30))
# Get some pixel-wise stats for the time series
irStdDev = allCollection.select(shadowSumBands).reduce(ee.Reducer.stdDev())
irMean = allCollection.select(shadowSumBands).reduce(ee.Reducer.mean())
# Mask out dark dark outliers
collection_tdom = collection.map(TDOM)
return collection_tdom.map(mask)
def getTopo(self,img):
''' funtion to filter for areas with terrain and areas without'''
dem = self.env.dem.unmask(0)
geom = ee.Geometry(img.get('system:footprint')).bounds()
slp_rad = ee.Terrain.slope(dem).clip(geom);
slope = slp_rad.reduceRegion(reducer= ee.Reducer.percentile([80]), \
geometry= geom,\
scale= 100 ,\
maxPixels=10000000)
return img.set('slope',slope.get('slope'))
def scaleS2(self,img):
divideBands = ['B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B10','B11','B12']
bandNames = img.bandNames()
otherBands = bandNames.removeAll(divideBands)
others = img.select(otherBands)
out = img.select(divideBands).divide(10000)
return out.addBands(others).copyProperties(img,['system:time_start','system:footprint','MEAN_SOLAR_ZENITH_ANGLE','MEAN_SOLAR_AZIMUTH_ANGLE']).set("centroid",img.geometry().centroid());
def reScaleS2(self,img):
bandNames = img.bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = img.select(otherBands)
t = img.select(self.env.divideBands);
t = t.multiply(10000)
out = ee.Image(t.copyProperties(img).copyProperties(img,['system:time_start'])).addBands(others).int16()
return out;
def pixelArea(self,img):
geom = ee.Geometry(img.get('system:footprint')).bounds()
area = img.select(['red']).gt(0).reduceRegion(reducer= ee.Reducer.sum(),\
geometry= geom,\
scale= 100,\
maxPixels=10000000)
return img.set("pixelArea",area.get("red"))
# Function to mask clouds using the Sentinel-2 QA band.
def QAMaskCloud(self,img):
bandNames = img.bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = img.select(otherBands)
qa = img.select('QA60').int16();
img = img.select(self.env.divideBands)
# Bits 10 and 11 are clouds and cirrus, respectively.
cloudBitMask = int(math.pow(2, 10));
cirrusBitMask = int(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));
img = img.updateMask(mask).addBands(others)
# Return the masked and scaled data.
return img
def sentinelCloudScore(self,img):
"""
Computes spectral indices of cloudyness and take the minimum of them.
Each spectral index is fairly lenient because the group minimum
is a somewhat stringent comparison policy. side note -> this seems like a job for machine learning :)
originally written by Matt Hancher for Landsat imagery
adapted to Sentinel by Chris Hewig and Ian Housman
"""
def rescale(img, thresholds):
"""
Linear stretch of image between two threshold values.
"""
return img.subtract(thresholds[0]).divide(thresholds[1] - thresholds[0])
# cloud until proven otherwise
score = ee.Image(1)
blueCirrusScore = ee.Image(0)
# clouds are reasonably bright
blueCirrusScore = blueCirrusScore.max(rescale(img.select(['blue']), [0.1, 0.5]))
blueCirrusScore = blueCirrusScore.max(rescale(img.select(['cb']), [0.1, 0.5]))
blueCirrusScore = blueCirrusScore.max(rescale(img.select(['cirrus']), [0.1, 0.3]))
score = score.min(blueCirrusScore)
score = score.min(rescale(img.select(['red']).add(img.select(['green'])).add(img.select('blue')), [0.2, 0.8]))
score = score.min(rescale(img.select(['nir']).add(img.select(['swir1'])).add(img.select('swir2')), [0.3, 0.8]))
# clouds are moist
ndsi = img.normalizedDifference(['green','swir1'])
score=score.min(rescale(ndsi, [0.8, 0.6]))
score = score.multiply(100).byte();
score = score.clamp(0,100);
return img.addBands(score.rename(['cloudScore']))
def cloudMasking(self,collection):
def maskClouds(img):
cloudMask = img.select(['cloudScore']).lt(self.env.cloudScoreThresh)\
.focal_min(self.env.dilatePixels) \
.focal_max(self.env.contractPixels) \
.rename(['cloudMask'])
bandNames = img.bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = img.select(otherBands)
img = img.select(self.env.divideBands).updateMask(cloudMask)
return img.addBands(cloudMask).addBands(others);
# Find low cloud score pctl for each pixel to avoid comission errors
minCloudScore = collection.select(['cloudScore']).reduce(ee.Reducer.percentile([self.env.cloudScorePctl]));
collection = collection.map(maskClouds)
return collection
def brdf(self,img):
def _apply(image, kvol, kvol0):
blue = _correct_band(image, 'blue', kvol, kvol0, f_iso=0.0774, f_geo=0.0079, f_vol=0.0372)
green = _correct_band(image, 'green', kvol, kvol0, f_iso=0.1306, f_geo=0.0178, f_vol=0.0580)
red = _correct_band(image, 'red', kvol, kvol0, f_iso=0.1690, f_geo=0.0227, f_vol=0.0574)
re1 = _correct_band(image, 're1', kvol, kvol0, f_iso=0.2085, f_geo=0.0256, f_vol=0.0845)
re2 = _correct_band(image, 're2', kvol, kvol0, f_iso=0.2316, f_geo=0.0273, f_vol=0.1003)
re3 = _correct_band(image, 're3', kvol, kvol0, f_iso=0.2599, f_geo=0.0294, f_vol=0.1197)
nir = _correct_band(image, 'nir', kvol, kvol0, f_iso=0.3093, f_geo=0.0330, f_vol=0.1535)
re4 = _correct_band(image, 're4', kvol, kvol0, f_iso=0.2907, f_geo=0.0410, f_vol=0.1611)
swir1 = _correct_band(image, 'swir1', kvol, kvol0, f_iso=0.3430, f_geo=0.0453, f_vol=0.1154)
swir2 = _correct_band(image, 'swir2', kvol, kvol0, f_iso=0.2658, f_geo=0.0387, f_vol=0.0639)
return replace_bands(image, [blue, green, red,re1,re2,re3, nir,re4, swir1, swir2])
def _correct_band(image, band_name, kvol, kvol0, f_iso, f_geo, f_vol):
"""fiso + fvol * kvol + fgeo * kgeo"""
iso = ee.Image(f_iso)
geo = ee.Image(f_geo)
vol = ee.Image(f_vol)
pred = vol.multiply(kvol).add(geo.multiply(kvol)).add(iso).rename(['pred'])
pred0 = vol.multiply(kvol0).add(geo.multiply(kvol0)).add(iso).rename(['pred0'])
cfac = pred0.divide(pred).rename(['cfac'])
corr = image.select(band_name).multiply(cfac).rename([band_name])
return corr
def _kvol(sunAz, sunZen, viewAz, viewZen):
"""Calculate kvol kernel.
From Lucht et al. 2000
Phase angle = cos(solar zenith) cos(view zenith) + sin(solar zenith) sin(view zenith) cos(relative azimuth)"""
relative_azimuth = sunAz.subtract(viewAz).rename(['relAz'])
pa1 = viewZen.cos() \
.multiply(sunZen.cos())
pa2 = viewZen.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle1 = pa1.add(pa2)
phase_angle = phase_angle1.acos()
p1 = ee.Image(PI().divide(2)).subtract(phase_angle)
p2 = p1.multiply(phase_angle1)
p3 = p2.add(phase_angle.sin())
p4 = sunZen.cos().add(viewZen.cos())
p5 = ee.Image(PI().divide(4))
kvol = p3.divide(p4).subtract(p5).rename(['kvol'])
viewZen0 = ee.Image(0)
pa10 = viewZen0.cos() \
.multiply(sunZen.cos())
pa20 = viewZen0.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle10 = pa10.add(pa20)
phase_angle0 = phase_angle10.acos()
p10 = ee.Image(PI().divide(2)).subtract(phase_angle0)
p20 = p10.multiply(phase_angle10)
p30 = p20.add(phase_angle0.sin())
p40 = sunZen.cos().add(viewZen0.cos())
p50 = ee.Image(PI().divide(4))
kvol0 = p30.divide(p40).subtract(p50).rename(['kvol0'])
return (kvol, kvol0)
date = img.date()
footprint = ee.List(img.geometry().bounds().bounds().coordinates().get(0));
(sunAz, sunZen) = sun_angles.create(date, footprint)
(viewAz, viewZen) = view_angles.create(footprint)
(kvol, kvol0) = _kvol(sunAz, sunZen, viewAz, viewZen)
bandNames = img.bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = img.select(otherBands)
img = ee.Image(_apply(img, kvol.multiply(PI()), kvol0.multiply(PI())))
return img
def terrain(self,img):
degree2radian = 0.01745;
geom = ee.Geometry(img.get('system:footprint')).bounds().buffer(10000)
dem = self.env.dem
bandNames = img.bandNames()
otherBands = bandNames.removeAll(self.env.divideBands)
others = img.select(otherBands)
bandList = ['blue','green','red','re1','re2','re3','nir','re4','cb','cirrus','swir1','swir2','waterVapor']
def topoCorr_IC(img):
# Extract image metadata about solar position
SZ_rad = ee.Image.constant(ee.Number(img.get('MEAN_SOLAR_ZENITH_ANGLE'))).multiply(degree2radian).clip(geom);
SA_rad = ee.Image.constant(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE'))).multiply(degree2radian).clip(geom);
# Creat terrain layers
slp = ee.Terrain.slope(dem).clip(geom);
slp_rad = ee.Terrain.slope(dem).multiply(degree2radian).clip(geom);
asp_rad = ee.Terrain.aspect(dem).multiply(degree2radian).clip(geom);
# Calculate the Illumination Condition (IC)
# slope part of the illumination condition
cosZ = SZ_rad.cos();
cosS = slp_rad.cos();
slope_illumination = cosS.expression("cosZ * cosS", \
{'cosZ': cosZ, 'cosS': cosS.select('slope')});
# aspect part of the illumination condition
sinZ = SZ_rad.sin();
sinS = slp_rad.sin();
cosAziDiff = (SA_rad.subtract(asp_rad)).cos();
aspect_illumination = sinZ.expression("sinZ * sinS * cosAziDiff", \
{'sinZ': sinZ, \
'sinS': sinS, \
'cosAziDiff': cosAziDiff});
# full illumination condition (IC)
ic = slope_illumination.add(aspect_illumination);
# Add IC to original image
img_plus_ic = ee.Image(img.addBands(ic.rename(['IC'])).addBands(cosZ.rename(['cosZ'])).addBands(cosS.rename(['cosS'])).addBands(slp.rename(['slope'])));
return ee.Image(img_plus_ic);
def topoCorr_SCSc(img):
img_plus_ic = img;
mask1 = img_plus_ic.select('nir').gt(-0.1);