def test_6s_example1(self):
# Implements Example_In_1.txt from the 6S distribution in Py6S
# Tests against manual run of that file
s = SixS()
s.geometry = Geometry.User()
s.geometry.solar_z = 40
s.geometry.solar_a = 100
s.geometry.view_z = 45
s.geometry.view_a = 50
s.geometry.month = 7
s.geometry.day = 23
s.atmos_profile = AtmosProfile.UserWaterAndOzone(3.0, 3.5)
s.aero_profile = AeroProfile.User(dust=0.25,
water=0.25,
oceanic=0.25,
soot=0.25)
s.aot550 = 0.5
s.altitudes.set_target_custom_altitude(0.2)
s.altitudes.set_sensor_custom_altitude(3.3, aot=0.25)
s.wavelength = Wavelength(PredefinedWavelengths.AVHRR_NOAA9_B1)
s.ground_reflectance = GroundReflectance.HeterogeneousLambertian(
0.5, GroundReflectance.ClearWater,
GroundReflectance.GreenVegetation)
s.atmos_corr = AtmosCorr.AtmosCorrBRDFFromReflectance(0.1)
s.run()
self.assertAlmostEqual(s.outputs.apparent_radiance,
12.749,
delta=0.002)
def test_atmos_profile(self):
aps = [
AtmosProfile.Tropical,
AtmosProfile.NoGaseousAbsorption,
AtmosProfile.UserWaterAndOzone(0.9, 3),
]
results = [0.2723143, 0.2747224, 0.2476101]
for i in range(len(aps)):
s = SixS()
s.atmos_profile = aps[i]
s.run()
self.assertAlmostEqual(
s.outputs.apparent_reflectance,
results[i],
msg="Error in atmos profile with ID %s. Got %f, expected %f." %
(str(aps[i]), s.outputs.apparent_reflectance, results[i]),
delta=0.002,
)
def test_6s_example4(self):
s = SixS()
s.geometry = Geometry.User()
s.geometry.solar_z = 61.23
s.geometry.solar_a = 18.78
s.geometry.solar_a = 0
s.geometry.view_z = 18.78
s.geometry.view_a = 159.2
s.geometry.month = 4
s.geometry.day = 30
s.atmos_profile = AtmosProfile.UserWaterAndOzone(0.29, 0.41)
s.aero_profile = AeroProfile.PredefinedType(AeroProfile.Continental)
s.aot550 = 0.14
s.altitudes.set_target_sea_level()
s.altitudes.set_sensor_satellite_level()
s.wavelength = Wavelength(PredefinedWavelengths.AVHRR_NOAA11_B1)
s.ground_reflectance = GroundReflectance.HomogeneousLambertian([
0.827,
0.828,
0.828,
0.827,
0.827,
0.827,
0.827,
0.826,
0.826,
0.826,
0.826,
0.825,
0.826,
0.826,
0.827,
0.827,
0.827,
0.827,
0.828,
0.828,
0.828,
0.829,
0.829,
0.828,
0.826,
0.826,
0.825,
0.826,
0.826,
0.826,
0.827,
0.827,
0.827,
0.826,
0.825,
0.826,
0.828,
0.829,
0.830,
0.831,
0.833,
0.834,
0.835,
0.836,
0.836,
0.837,
0.838,
0.838,
0.837,
0.836,
0.837,
0.837,
0.837,
0.840,
0.839,
0.840,
0.840,
0.841,
0.841,
0.841,
0.841,
0.842,
0.842,
0.842,
0.842,
0.843,
0.842,
0.843,
0.843,
0.843,
0.843,
0.843,
0.843,
0.841,
0.841,
0.842,
0.842,
0.842,
0.842,
0.842,
0.841,
0.840,
0.841,
0.838,
0.839,
0.837,
0.837,
0.836,
0.832,
0.832,
0.830,
0.829,
0.826,
0.826,
0.824,
0.821,
0.821,
0.818,
0.815,
0.813,
0.812,
0.811,
0.810,
0.808,
0.807,
0.807,
0.812,
0.808,
0.806,
0.807,
0.807,
0.807,
0.807,
])
s.run()
self.assertAlmostEqual(s.outputs.apparent_radiance,
170.771,
delta=0.002)
def run_py6s(self, geom, acqui_date, option=1):
"""
runs the 6S algorithm for atmospheric correction on a user-defined
geometry and date on Sentinel-2 imagery
Requires Google Earth-Engine Python API client
Parameters
----------
geom : EE-Geometry
Google EE geometry specify the geographic extent to be processed
acqui_date : String
date (YYYY-MM-dd) of the desired scene to be processed
option : Integer
for computing the cloud mask the user can decide whether the
ESA delivered quality layer should be used for cloud masking
(option=1) or an alternative approach originally developed for
Landsat TM (option=2) should be used (Def=1)
Returns
-------
s2_surf : ee.image.Image
Google EE image instance with surface reflectance values
and two additional bands containing Clouds ('CloudMask') and
detected cloud shadows ('CloudShadows')
"""
date = ee.Date(acqui_date)
# get the Sentinel-2 image at or immediately after the specified date
self.S2 = ee.Image(
ee.ImageCollection('COPERNICUS/S2')
.filterBounds(geom)
.filterDate(date,date.advance(3,'month'))
.sort('system:time_start')
.first()
)
# extract the relevant metadata for carrying out the atmospheric correction
self.info = self.S2.getInfo()['properties']
# get the solar zenith angle an the scene data
self.scene_date = datetime.datetime.utcfromtimestamp(
self.info['system:time_start']/1000)
self.solar_z = self.info['MEAN_SOLAR_ZENITH_ANGLE']
# log the identifier of the processed scene
scene_id = self.info.get('DATASTRIP_ID')
self.__logger.info("Starting Processing scene '{}' using GEE and Py6S".format(
scene_id))
# get the atmospheric constituents
# i.e water vapor (h2o), ozone (o3), aerosol optical thickness (aot)
h2o = Atmospheric.water(geom, date).getInfo()
o3 = Atmospheric.ozone(geom, date).getInfo()
aot = Atmospheric.aerosol(geom, date).getInfo()
# add this information to the info dictionary as this metadata could
# be of interest afterwards
self.info['WATER_VAPOUR'] = h2o
self.info['OZONE'] = o3
self.info['AOT'] = aot
# get the average altitude of the region to be processed
# for the Digital Elevation Model (DEM) the Shuttle Radar Topography
# Mission (SRTM) is used (Version 4) as it covers most parts of the
# Earth (90 arc-sec data is used)
SRTM = ee.Image('CGIAR/SRTM90_V4')
alt = SRTM.reduceRegion(reducer = ee.Reducer.mean(),
geometry = geom.centroid()).get('elevation').getInfo()
message = "Atcorr-Metadata: Water-Vapor = {0}, Ozone = {1}, AOT = {2}, "\
" Average Altitude (m) = {3}".format(
h2o, o3, aot, alt)
self.__logger.info(message)
# Py6S uses units of kilometers
km = alt/1000
# mask out clouds and cirrus from the imagery using the cloud score
# optionally
# algorithm provided by Sam Murhpy under Apache 2.0 licence
# see: https://github.com/samsammurphy/cloud-masking-sentinel2/blob/master/cloud-masking-sentinel2.ipynb
# also converts the image values to top-of-atmosphere reflectance
self.__logger.info('Calculating cloud and shadow mask')
self.S2 = mask_clouds(self.S2, option=1)
self.__logger.info('Finished calculating cloud and shadow mask')
# create a 6S object from the Py6S class
# Instantiate (use the explizit path to installation directory of the
# 6S binary as otherwise there might be an error)
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 (simplification!)
s.geometry.solar_z = self.solar_z # solar zenith angle
s.geometry.month = self.scene_date.month # month and day used for Earth-Sun distance
s.geometry.day = self.scene_date.day # month and day used for Earth-Sun distance
s.altitudes.set_sensor_satellite_level()
s.altitudes.set_target_custom_altitude(km)
self.__logger.info('6S: Starting processing of Sentinel-2 scene!')
# now iterate over the nine relevant Sentinel-2 bands to perform the
# atmospheric correction and get the surface reflectance
# go through the spectral bands
B2_surf = self.surface_reflectance(s, 'B2')
self.__logger.info('6S: Finished processing Sentinel-2 Band 2!')
B3_surf = self.surface_reflectance(s, 'B3')
self.__logger.info('6S: Finished processing Sentinel-2 Band 3!')
B4_surf = self.surface_reflectance(s, 'B4')
self.__logger.info('6S: Finished processing Sentinel-2 Band 4!')
B5_surf = self.surface_reflectance(s, 'B5')
self.__logger.info('6S: Finished processing Sentinel-2 Band 5!')
B6_surf = self.surface_reflectance(s, 'B6')
self.__logger.info('6S: Finished processing Sentinel-2 Band 6!')
B7_surf = self.surface_reflectance(s, 'B7')
self.__logger.info('6S: Finished processing Sentinel-2 Band 7!')
B8A_surf = self.surface_reflectance(s, 'B8A')
self.__logger.info('6S: Finished processing Sentinel-2 Band 8A!')
B11_surf = self.surface_reflectance(s, 'B11')
self.__logger.info('6S: Finished processing Sentinel-2 Band 11!')
B12_surf = self.surface_reflectance(s, 'B12')
self.__logger.info('6S: Finished processing Sentinel-2 Band 12!')
self.__logger.info('6S: Finished processing of Sentinel-2 scene!')
# make a stack of the spectral bands
# also add the computed cloud and shadow mask
cm = self.S2.select('CloudMask')
sm = self.S2.select('ShadowMask')
S2_surf = B2_surf.addBands(B3_surf).addBands(B4_surf).addBands(B5_surf).addBands(B6_surf).addBands(B7_surf).addBands(B8A_surf).addBands(B11_surf).addBands(B12_surf).addBands(cm).addBands(sm)
# return the surface reflectance image
close_logger(self.__logger)
return S2_surf