def extractor(image):
# a time
date = ee.Date(image.get('system:time_start'))
# a place
if not Atmcorr_input.geom:
Atmcorr_input.geom = image.geometry().centroid()
# atmospheric correction inputs
atmcorr_inputs = ee.Dictionary({
'solar_z':image.get('MEAN_SOLAR_ZENITH_ANGLE'),
'h2o':Atmospheric.water(Atmcorr_input.geom,date),
'o3':Atmospheric.ozone(Atmcorr_input.geom,date),
'aot':Atmospheric.aerosol(Atmcorr_input.geom,date),
'alt':Atmcorr_input.elevation.reduceRegion(\
reducer = ee.Reducer.mean(),\
geometry = Atmcorr_input.geom.centroid()).get('be75'),
'doy':Atmcorr_input.doy_from_date(date)
})
# solar irradiance (to calculate at-sensor radiance from TOA)
solar_irradiance = {
'B1': image.get('SOLAR_IRRADIANCE_B1'),
'B2': image.get('SOLAR_IRRADIANCE_B2'),
'B3': image.get('SOLAR_IRRADIANCE_B3'),
'B4': image.get('SOLAR_IRRADIANCE_B4'),
'B5': image.get('SOLAR_IRRADIANCE_B5'),
'B6': image.get('SOLAR_IRRADIANCE_B6'),
'B7': image.get('SOLAR_IRRADIANCE_B7'),
'B8': image.get('SOLAR_IRRADIANCE_B8'),
'B8A': image.get('SOLAR_IRRADIANCE_B8A'),
'B9': image.get('SOLAR_IRRADIANCE_B9'),
'B10': image.get('SOLAR_IRRADIANCE_B10'),
'B11': image.get('SOLAR_IRRADIANCE_B11'),
'B12': image.get('SOLAR_IRRADIANCE_B12')
}
# image identifier
imgID = image.get('system:index')
# properties
properties = {'imgID':imgID,\
'bandNames':['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7',\
'B8', 'B8A', 'B9', 'B10', 'B11', 'B12'],\
'atmcorr_inputs':atmcorr_inputs,\
'solar_irradiance':solar_irradiance}
return ee.Feature(Atmcorr_input.geom, properties)
def get():
altitude = AtmcorrInput.elevation.reduceRegion(\
reducer = ee.Reducer.mean(),\
geometry = TimeSeries.geom.centroid()\
)
return ee.Dictionary({
'solar_z':
mission_specifics.solar_z(TimeSeries.image, TimeSeries.mission),
'h2o':
Atmospheric.water(TimeSeries.geom, TimeSeries.date),
'o3':
Atmospheric.ozone(TimeSeries.geom, TimeSeries.date),
'aot':
Atmospheric.aerosol(TimeSeries.geom, TimeSeries.date),
'alt':
altitude.get('be75'),
'doy':
TimeSeries.day_of_year
})
# metadata
info = S2.getInfo()['properties']
scene_date = datetime.datetime.utcfromtimestamp(
info['system:time_start'] /
1000) # i.e. Python uses seconds, EE uses milliseconds
date = ee.Date(scene_date)
solar_z = info['MEAN_SOLAR_ZENITH_ANGLE']
#需要修改
geom = ee.Geometry.Point(125.266, 42.86)
toa = S2.divide(10000)
print(2)
"""atmospheric constituents"""
h2o = Atmospheric.water(geom, date).getInfo()
o3 = Atmospheric.ozone(geom, date).getInfo()
aot = Atmospheric.aerosol(geom, date).getInfo()
"""target altitude (km)"""
SRTM = ee.Image(
'CGIAR/SRTM90_V4'
) # Shuttle Radar Topography mission covers *most* of the Earth
alt = SRTM.reduceRegion(reducer=ee.Reducer.mean(),
geometry=geom.centroid()).get('elevation').getInfo()
km = alt / 1000 # i.e. Py6S uses units of kilometers
"""6S object"""
# Instantiate
s = SixS()
# Atmospheric constituents
s.atmos_profile = AtmosProfile.UserWaterAndOzone(h2o, o3)
def TOAtoSR(self, img):
#TDOMMask = img.select(['TDOMMask'])
info = self.collectionMeta[self.env.feature]['properties']
scene_date = datetime.datetime.utcfromtimestamp(
info['system:time_start'] /
1000) # i.e. Python uses seconds, EE uses milliseconds
solar_z = info['MEAN_SOLAR_ZENITH_ANGLE']
geom = ee.Geometry(info['system:footprint']).centroid()
#geom = ee.Geometry.Point([info['centroid']['coordinates'][0],info['centroid']['coordinates'][1]])
date = ee.Date.fromYMD(scene_date.year, scene_date.month,
scene_date.day)
h2o = Atmospheric.water(geom, date).getInfo()
o3 = Atmospheric.ozone(geom, date).getInfo()
aot = Atmospheric.aerosol(geom, date).getInfo()
SRTM = ee.Image(
'CGIAR/SRTM90_V4'
) # Shuttle Radar Topography mission covers *most* of the Earth
alt = SRTM.reduceRegion(reducer=ee.Reducer.mean(),
geometry=geom).get('elevation').getInfo()
if alt:
km = alt / 1000 # i.e. Py6S uses units of kilometers
else:
km = 0
# Instantiate
s = SixS()
# Atmospheric constituents
s.atmos_profile = AtmosProfile.UserWaterAndOzone(h2o, o3)
s.aero_profile = AeroProfile.Continental
s.aot550 = aot
# Earth-Sun-satellite geometry
s.geometry = Geometry.User()
s.geometry.view_z = 0 # always NADIR (I think..)
s.geometry.solar_z = solar_z # solar zenith angle
s.geometry.month = scene_date.month # month and day used for Earth-Sun distance
s.geometry.day = scene_date.day # month and day used for Earth-Sun distance
s.altitudes.set_sensor_satellite_level()
s.altitudes.set_target_custom_altitude(km)
def spectralResponseFunction(bandname):
"""
Extract spectral response function for given band name
"""
bandSelect = {
'B1': PredefinedWavelengths.S2A_MSI_01,
'B2': PredefinedWavelengths.S2A_MSI_02,
'B3': PredefinedWavelengths.S2A_MSI_03,
'B4': PredefinedWavelengths.S2A_MSI_04,
'B5': PredefinedWavelengths.S2A_MSI_05,
'B6': PredefinedWavelengths.S2A_MSI_06,
'B7': PredefinedWavelengths.S2A_MSI_07,
'B8': PredefinedWavelengths.S2A_MSI_08,
'B8A': PredefinedWavelengths.S2A_MSI_09,
'B9': PredefinedWavelengths.S2A_MSI_10,
'B10': PredefinedWavelengths.S2A_MSI_11,
'B11': PredefinedWavelengths.S2A_MSI_12,
'B12': PredefinedWavelengths.S2A_MSI_13
}
return Wavelength(bandSelect[bandname])
def toa_to_rad(bandname):
"""
Converts top of atmosphere reflectance to at-sensor radiance
"""
# solar exoatmospheric spectral irradiance
ESUN = info['SOLAR_IRRADIANCE_' + bandname]
solar_angle_correction = math.cos(math.radians(solar_z))
# Earth-Sun distance (from day of year)
doy = scene_date.timetuple().tm_yday
d = 1 - 0.01672 * math.cos(
0.9856 * (doy - 4)
) # http://physics.stackexchange.com/questions/177949/earth-sun-distance-on-a-given-day-of-the-year
# conversion factor
multiplier = ESUN * solar_angle_correction / (math.pi * d**2)
# at-sensor radiance
rad = img.select(bandname).multiply(multiplier)
return rad
def surface_reflectance(bandname):
"""
Calculate surface reflectance from at-sensor radiance given waveband name
"""
# run 6S for this waveband
s.wavelength = spectralResponseFunction(bandname)
s.run()
# extract 6S outputs
Edir = s.outputs.direct_solar_irradiance #direct solar irradiance
Edif = s.outputs.diffuse_solar_irradiance #diffuse solar irradiance
Lp = s.outputs.atmospheric_intrinsic_radiance #path radiance
absorb = s.outputs.trans[
'global_gas'].upward #absorption transmissivity
scatter = s.outputs.trans[
'total_scattering'].upward #scattering transmissivity
tau2 = absorb * scatter #total transmissivity
# radiance to surface reflectance
rad = toa_to_rad(bandname)
ref = rad.subtract(Lp).multiply(math.pi).divide(tau2 *
(Edir + Edif))
return ref
# all wavebands
output = img.select('QA60')
for band in [
'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9',
'B10', 'B11', 'B12'
]:
output = output.addBands(surface_reflectance(band))
self.env.feature += 1
return output.copyProperties(img, [
'system:time_start', 'system:footprint', 'MEAN_SOLAR_ZENITH_ANGLE',
'MEAN_SOLAR_AZIMUTH_ANGLE'
])
def correctAts(self,image):
keepBands = ['temp']
esun = {'TM': [1958.00,1827.00,1551.00,1036.00,214.90,80.65],
'ETM':[1969.00,1840.00,1551.00,1044.00,225.70,82.07],
'OLI':[2004.57,1820.75,1549.49, 951.76,247.55,85.46]}
exportBands = ['blue','green','red','nir','swir1','swir2']
metaData = self.collectionMeta[self.feature]['properties']
sensorId = str(metaData['SENSOR_ID'])
if len(sensorId) > 3:
sensorId = sensorId.split('_')[0]
sunAngle = 90-float(metaData['SUN_ELEVATION'])
llLat = float(metaData['CORNER_LL_LAT_PRODUCT'])
llLon = float(metaData['CORNER_LL_LON_PRODUCT'])
ulLat = float(metaData['CORNER_UL_LAT_PRODUCT'])
ulLon = float(metaData['CORNER_UL_LON_PRODUCT'])
lrLat = float(metaData['CORNER_LR_LAT_PRODUCT'])
lrLon = float(metaData['CORNER_LR_LON_PRODUCT'])
urLat = float(metaData['CORNER_UR_LAT_PRODUCT'])
urLon = float(metaData['CORNER_UR_LON_PRODUCT'])
imgBounds = ee.Geometry.Polygon([[llLon,llLat],[ulLon,ulLat],[urLon,urLat],[lrLon,lrLat],[llLon,llLat]])
elv = ee.Image('CGIAR/SRTM90_V4')
srtm = ee.Image(0).where(elv.gt(0),elv)
#minAlt = np.abs(srtm.reduceRegion(reducer=ee.Reducer.min(),scale=90,geometry=imgBounds,maxPixels=9e9).get('elevation').getInfo())
maxAlt = np.abs(srtm.reduceRegion(reducer=ee.Reducer.max(),scale=90,geometry=imgBounds,maxPixels=9e9).get('constant').getInfo())
acqDate = metaData['DATE_ACQUIRED'].split('-')
dateob = datetime.date(int(acqDate[0]),int(acqDate[1]),int(acqDate[2]))
eeDate = ee.Date.fromYMD(int(acqDate[0]),int(acqDate[1]),int(acqDate[2]))
doy = dateob.timetuple().tm_yday
orbit_correction = 0.03275104*math.cos(doy/59.66638337) + 0.96804905
dsun = (1 - 0.01672 * math.cos(0.9856 * (doy - 4))) ** 2
atmoData = Atmospheric(imgBounds,eeDate)
aot = atmoData.aerosol().getInfo()
h2o = atmoData.water().getInfo()
o3 = atmoData.ozone().getInfo()
imgList = []
altRange = np.arange(0,maxAlt+60,15)
for i in range(altRange.size):
elvMask = srtm.gte(altRange[i]-7.5).And(srtm.lt(altRange[i]+7.5))
temp = image.updateMask(elvMask)
outsr = image.select(keepBands)
for j in range(len(self.bands)):
a,b = self.luts[sensorId][self.bands[j]](sunAngle,h2o,o3,aot,(altRange[i]/1000.))
band = temp.select(self.bands[j])
denom = esun[sensorId][j]*math.cos(math.radians(sunAngle))
numer = math.pi * dsun
radiance = band.multiply(ee.Number(denom)).divide(ee.Number(numer))
a *= orbit_correction
b *= orbit_correction
outsr = outsr.addBands((radiance.subtract(ee.Number(a)).divide(ee.Number(b))\
.rename([self.bands[j]])))
imgList.append(outsr.where(outsr.lt(0),0))
outImg = ee.ImageCollection(imgList).median().copyProperties(image,['system:time_start'])
self.feature += 1
return outImg