def radianceFromTOA():
"""
calculate at-sensor radiance from top-of-atmosphere (TOA) reflectance
"""
# top of atmosphere reflectance
toa = mission_specifics.TOA(TimeSeries.image, TimeSeries.mission)
# solar irradiances
ESUNs = mission_specifics.ESUNs(TimeSeries.image, TimeSeries.mission)
# wavebands
bands = mission_specifics.ee_bandnames(TimeSeries.mission)
# solar zenith (radians)
theta = mission_specifics.solar_z(
TimeSeries.image, TimeSeries.mission).multiply(0.017453293)
# circular math
pi = ee.Number(3.14159265359)
# Earth-Sun distance squared (AU)
d = ee.Number(TimeSeries.day_of_year).subtract(4).multiply(
0.017202).cos().multiply(-0.01672).add(1)
d_squared = d.multiply(d)
# radiace at-sensor
rad = toa.select(ee.List(bands)).multiply(ESUNs).multiply(
theta.cos()).divide(pi).divide(d_squared)
return rad
def extractAllTimeSeries(target,
geom,
startDate,
stopDate,
missions,
removeClouds=True):
"""
Extracts time series for each mission and join them together
"""
# will store results here (and use consistent band names)
allTimeSeries = {
'blue': [],
'green': [],
'red': [],
'nir': [],
'swir1': [],
'swir2': [],
'timeStamp': []
}
# for mission in ['Landsat4']:
for mission in missions:
timeseries = timeseries_extrator(geom,
startDate,
stopDate,
mission,
removeClouds=removeClouds)
# names of wavebands
eeNames = ee_bandnames(mission)
commonNames = common_bandnames(mission)
# add mission to timeseries
for key in timeseries.keys():
if key[0] == 'B':
commonName = commonNames[eeNames.index(key)]
if commonName in allTimeSeries.keys():
allTimeSeries[commonName].append(timeseries[key])
if key == 'timeStamp':
allTimeSeries['timeStamp'].append(timeseries['timeStamp'])
# flatten each variables (from separate missions) into a single list
def flatten(multilist):
if isinstance(multilist[0], list):
return [item for sublist in multilist for item in sublist]
else:
return multilist
for key in allTimeSeries.keys():
allTimeSeries[key] = flatten(allTimeSeries[key])
return allTimeSeries
def surface_reflectance_timeseries(meanRadiance, iLUTs, mission):
"""
Atmospherically corrects mean (cloud-free) pixel radiances
returning a time series of surface reflectance values.
"""
feature_collection = meanRadiance['features']
# band names
ee_bandnames = mission_specifics.ee_bandnames(mission)
py6s_bandnames = mission_specifics.py6s_bandnames(mission)
# time series output variable
timeSeries = {'timeStamp': [], 'mission': mission}
for ee_bandname in ee_bandnames:
timeSeries[ee_bandname] = []
# atmospherically correct each scene in collection
for feature in feature_collection:
# time stamp
timeSeries['timeStamp'].append(feature['properties']['timeStamp'])
# mean average pixel radiances
mean_averages = feature['properties']['mean_averages']
# atmospheric correction inputs
atmcorr_inputs = feature['properties']['atmcorr_inputs']
solar_z = atmcorr_inputs['solar_z'] # solar zenith [degrees]
h2o = atmcorr_inputs['h2o'] # water vapour column
o3 = atmcorr_inputs['o3'] # ozone
aot = atmcorr_inputs['aot'] # aerosol optical thickness
alt = atmcorr_inputs['alt'] # altitude (above sea level, [km])
day_of_year = atmcorr_inputs['doy'] # i.e. Jan 1st = 1
# atmospheric correction (each waveband)
for i, ee_bandname in enumerate(ee_bandnames):
radiance = mean_averages[ee_bandname]
iLUT = iLUTs.iLUTs[py6s_bandnames[i]]
perihelion = iLUT(solar_z, h2o, o3, aot, alt)
timeSeries[ee_bandname].append(
atmcorr(radiance, perihelion, day_of_year))
return timeSeries