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l8_aerosol.py
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#/usr/bin/env python
import os
import sys
sys.path.insert(0, 'python')
import gdal
import json
import datetime
import logging
import numpy as np
from grab_l8_toa import read_l8
from ddv import ddv
from glob import glob
from scipy import signal, ndimage
import cPickle as pkl
from osgeo import osr
#from smoothn import smoothn
from multi_process import parmap
from scipy.ndimage import binary_dilation, binary_erosion
from reproject import reproject_data
from scipy.interpolate import griddata
from grab_brdf import MCD43_SurRef
from atmo_solver import solving_atmo_paras
from grab_uncertainty import grab_uncertainty
from psf_optimize import psf_optimize
from spatial_mapping import Find_corresponding_pixels
import warnings
warnings.filterwarnings("ignore")
from scipy.stats import linregress
class solve_aerosol(object):
'''
Prepareing modis data to be able to pass into
atmo_cor for the retrieval of atmospheric parameters.
'''
def __init__(self,
year,
month,
day,
emus_dir = '/home/ucfafyi/DATA/Multiply/emus/',
mcd43_dir = '/data/selene/ucfajlg/Ujia/MCD43/',
l8_toa_dir = '/home/ucfafyi/DATA/S2_MODIS/l_data/',
global_dem = '/home/ucfafyi/DATA/Multiply/eles/global_dem.vrt',
cams_dir = '/home/ucfafyi/DATA/Multiply/cams/',
mod08_dir = '/home/ucfafyi/DATA/Multiply/mod08/',
myd08_dir = '/home/ucfafyi/DATA/Multiply/myd08/',
l8_tile = (123, 34),
l8_psf = None,
qa_thresh = 255,
aero_res = 1000, # resolution for aerosol retrival in meters should be larger than 500
):
self.year = year
self.month = month
self.day = day
self.date = datetime.datetime(self.year, self.month, self.day)
self.doy = self.date.timetuple().tm_yday
self.mcd43_dir = mcd43_dir
self.emus_dir = emus_dir
self.qa_thresh = qa_thresh
self.l8_toa_dir = l8_toa_dir
self.global_dem = global_dem
self.cams_dir = cams_dir
self.mod08_dir = mod08_dir
self.myd08_dir = myd08_dir
self.l8_tile = l8_tile
self.l8_psf = l8_psf
self.bands = [2, 3, 4, 5, 6, 7]
self.band_indexs = [1, 2, 3, 4, 5, 6]
self.boa_bands = [469, 555, 645, 869, 1640, 2130]
self.aero_res = aero_res
self.mcd43_tmp = '%s/MCD43A1.A%d%03d.%s.006.*.hdf'
self.spectral_transform = [[1.0425211806, 1.03763437575, 1.02046102587, 0.999167480738, 1.00072211685, 0.955317665361 ],
[0.000960797104206, -0.00263498369438, -0.00179952807464, 0.0018999624331, -0.0072213121738, 0.00782954328347]]
def _load_xa_xb_xc_emus(self,):
xap_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xap.pkl'%(self.sensor))[0]
xbp_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xbp.pkl'%(self.sensor))[0]
xcp_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xcp.pkl'%(self.sensor))[0]
f = lambda em: pkl.load(open(em, 'rb'))
self.emus = parmap(f, [xap_emu, xbp_emu, xcp_emu])
def gaussian(self, xstd, ystd, angle, norm = True):
win = 2*int(round(max(1.96*xstd, 1.96*ystd)))
winx = int(round(win*(2**0.5)))
winy = int(round(win*(2**0.5)))
xgaus = signal.gaussian(winx, xstd)
ygaus = signal.gaussian(winy, ystd)
gaus = np.outer(xgaus, ygaus)
r_gaus = ndimage.interpolation.rotate(gaus, angle, reshape=True)
center = np.array(r_gaus.shape)/2
cgaus = r_gaus[center[0]-win/2: center[0]+win/2, center[1]-win/2:center[1]+win/2]
if norm:
return cgaus/cgaus.sum()
else:
return cgaus
def _save_img(self, fnames, refs, example_file):
g = gdal.Open(example_file)
projection = g.GetProjection()
geotransform = g.GetGeoTransform()
bands_refs = zip(fnames, refs)
f = lambda band_ref: self._save_band(band_ref, projection = projection, geotransform = geotransform)
parmap(f, bands_refs)
def _save_band(self, band_ref, projection, geotransform):
fname, ref = band_ref
nx, ny = ref.shape
dst_ds = gdal.GetDriverByName('GTiff').Create(fname, ny, nx, 1, gdal.GDT_Float32)
dst_ds.SetGeoTransform(geotransform)
dst_ds.SetProjection(projection)
dst_ds.GetRasterBand(1).WriteArray(ref)
dst_ds.FlushCache()
dst_ds = None
def _mcd08_aot(self,):
temp = 'HDF4_EOS:EOS_GRID:"%s":mod08:Aerosol_Optical_Depth_Land_Ocean_Mean'
try:
g = gdal.Open(temp%glob('%s/MOD08_D3.A2016%03d.006.*.hdf'%(self.mod08_dir, self.doy))[0])
dat = reproject_data(g, self.example_file, outputType= gdal.GDT_Float32).data * g.GetRasterBand(1).GetScale() + g.GetRasterBand(1).GetOffset()
dat[dat<=0] = np.nan
dat[dat>1.7] = np.nan
mod08_aot = np.nanmean(dat)
except:
mod08_aot = np.nan
try:
g1 = gdal.Open(temp%glob('%s/MYD08_D3.A2016%03d.006.*.hdf'%(self.myd08_dir, self.doy))[0])
dat1 = reproject_data(g1, self.example_file, outputType= gdal.GDT_Float32).data * g1.GetRasterBand(1).GetScale() + g1.GetRasterBand(1).GetOffset()
dat1[dat1<=0] = np.nan
dat1[dat1>1.7] = np.nan
myd08_aot = np.nanmean(dat1)
except:
myd08_aot = np.nan
return mod08_aot, myd08_aot
def _get_psf(self,):
self.logger.info('No PSF parameters specified, start solving.')
xstd, ystd = 12., 20.
psf = psf_optimize(self.toa[-1].data, [self.Hx, self.Hy], np.ma.array(self.boa[-1]), self.boa_qa[-1], self.ecloud, 0.1, xstd=xstd, ystd= ystd)
xs, ys = psf.fire_shift_optimize()
ang = 0
self.logger.info('Solved PSF parameters are: %.02f, %.02f, %d, %d, %d, and the correlation is: %f.' \
%(xstd, ystd, 0, xs, ys, 1-psf.costs.min()))
return xstd, ystd, ang, xs, ys
def _extend_vals(self, val):
if val.ndim == 2:
temp = np.zeros((self.efull_res, self.efull_res))
temp[:] = np.nan
temp[:self.full_res[0], :self.full_res[1]] = val
elif val.ndim == 3:
temp = np.zeros((val.shape[0], self.efull_res, self.efull_res))
temp[:] = np.nan
temp[:, :self.full_res[0], :self.full_res[1]] = val
else:
raise IOError('Only two and three dimensions array is supported.')
return temp
def _l8_aerosol(self,):
self.logger.propagate = False
self.logger.info('Start to retrieve atmospheric parameters.')
l8 = read_l8(self.l8_toa_dir, self.l8_tile, self.year, self.month, self.day, bands = self.bands)
self.l8_header = l8.header
self.logger.info('Loading emulators.')
self._load_xa_xb_xc_emus()
self.logger.info('Find corresponding pixels between L8 and MODIS tiles')
self.example_file = self.l8_toa_dir + '/%s_b%d.tif'%(l8.header, 1)
if len(glob(self.l8_toa_dir + '/MCD43_%s.npz'%(l8.header))) == 0:
boa, unc, hx, hy, lx, ly, flist = MCD43_SurRef(self.mcd43_dir, self.example_file, \
self.year, self.doy, [l8.saa_sza, l8.vaa_vza],
sun_view_ang_scale=[0.01, 0.01], bands = [3,4,1,2,6,7], tolz=0.003)
np.savez(self.l8_toa_dir + 'MCD43_%s.npz'%l8.header, boa=boa, unc=unc, hx=hx, hy=hy, lx=lx, ly=ly, flist=flist)
else:
f = np.load(self.l8_toa_dir + 'MCD43_%s.npz'%l8.header)
boa, unc, hx, hy, lx, ly, flist = f['boa'], f['unc'], f['hx'], f['hy'], f['lx'], f['ly'], f['flist']
self.Hx, self.Hy = hx, hy
self.logger.info('Applying spectral transform.')
self.boa_qa = np.ma.array(unc)
self.boa = np.ma.array(boa)*np.array(self.spectral_transform)[0][...,None] + \
np.array(self.spectral_transform)[1][...,None]
self.logger.info('Reading in TOA reflectance.')
self.sen_time = l8.sen_time
self.cloud = l8._get_qa()
self.full_res = self.cloud.shape
self.ecloud = binary_erosion(self.cloud, structure=np.ones((3,3)).astype(bool), iterations=10).astype(bool)
border_mask = np.zeros(self.full_res).astype(bool)
border_mask[[0, -1], :] = True
border_mask[:, [0, -1]] = True
xstd, ystd = 12., 20.
ker_size = 2*int(round(max(1.96*xstd, 1.96*ystd)))
self.dcloud = binary_dilation(self.ecloud | border_mask, structure=np.ones((3,3)).astype(bool), iterations=ker_size/2+10).astype(bool)
self.logger.info('Getting elevation.')
ele_data = reproject_data(self.global_dem, self.example_file, outputType = gdal.GDT_Float32).data/1000.
mask = ~np.isfinite(ele_data)
self.ele = np.ma.array(ele_data, mask = mask)
self.ele[mask] = np.nan
self.logger.info('Getting pripors from ECMWF forcasts.')
self.aot, self.tcwv, self.tco3 = np.array(self._read_cams(self.example_file))
self.logger.info('Mean values of priors are: %.02f, %.02f, %.02f'%\
(np.nanmean(self.aot), np.nanmean(self.tcwv), np.nanmean(self.tco3)))
self.toa = l8._get_toa()
self.saa, self.sza, self.vaa, self.vza = l8._get_angles()
self.saa[self.saa.mask] = self.sza[self.sza.mask] = \
self.vaa[self.vaa.mask] = self.vza[self.vza.mask] = np.nan
self.logger.info('Getting DDV aot prior')
self._get_ddv_aot(self.toa, l8, self.tcwv, self.tco3, ele_data)
self.logger.info('Sorting data.')
self.block_size = int(np.ceil(1. * self.aero_res / 30.))
self.num_blocks = int(np.ceil(max(self.full_res) / (1. * self.block_size)))
self.efull_res = self.block_size * self.num_blocks
shape1 = (self.num_blocks, self.block_size, self.num_blocks, self.block_size)
shape2 = (self.vza.shape[0], self.num_blocks, self.block_size, self.num_blocks, self.block_size)
self.ele = np.nanmean(self._extend_vals(self.ele ).reshape(shape1), axis=(3,1))
self.aot = np.nanmean(self._extend_vals(self.aot ).reshape(shape1), axis=(3,1))
self.tcwv = np.nanmean(self._extend_vals(self.tcwv).reshape(shape1), axis=(3,1))
self.tco3 = np.nanmean(self._extend_vals(self.tco3).reshape(shape1), axis=(3,1))
self.saa = np.nanmean(self._extend_vals(self.saa ).reshape(shape2), axis=(4,2))
self.sza = np.nanmean(self._extend_vals(self.sza ).reshape(shape2), axis=(4,2))
self.vaa = np.nanmean(self._extend_vals(self.vaa ).reshape(shape2), axis=(4,2))
self.vza = np.nanmean(self._extend_vals(self.vza ).reshape(shape2), axis=(4,2))
self.aot_unc = np.ones(self.aot.shape) * 0.8
self.tcwv_unc = np.ones(self.tcwv.shape) * 0.2
self.tco3_unc = np.ones(self.tco3.shape) * 0.2
#mod08_aot, myd08_aot = self._mcd08_aot()
#self.logger.info('Mean values for priors are: %.02f, %.02f, %.02f and mod08 and myd08 aot are: %.02f, %.02f'%\
# (np.nanmean(self.aot), np.nanmean(self.tcwv), np.nanmean(self.tco3), mod08_aot, myd08_aot))
#if np.isnan(mod08_aot):
self.aot[:] = np.nanmean(self.aot)
#else:
# temp = np.zeros_like(self.aot)
# temp[:] = mod08_aot
# self.aot = temp
self.logger.info('Applying PSF model.')
if self.l8_psf is None:
xstd, ystd, ang, xs, ys = self._get_psf()
else:
xstd, ystd, ang, xs, ys = self.l8_psf
shifted_mask = np.logical_and.reduce(((self.Hx+int(xs)>=0),
(self.Hx+int(xs)<self.full_res[0]),
(self.Hy+int(ys)>=0),
(self.Hy+int(ys)<self.full_res[1])))
self.Hx, self.Hy = self.Hx[shifted_mask]+int(xs), self.Hy[shifted_mask]+int(ys)
self.boa = self.boa [:, shifted_mask]
self.boa_qa = self.boa_qa[:, shifted_mask]
self.logger.info('Getting the convolved TOA reflectance.')
self.bad_pixs = self.dcloud[self.Hx, self.Hy]
ker = self.gaussian(xstd, ystd, ang)
f = lambda img: signal.fftconvolve(img, ker, mode='same')[self.Hx, self.Hy]
self.toa = np.array(parmap(f, list(self.toa)))
qua_mask = np.all(self.boa_qa <= self.qa_thresh, axis = 0)
boa_mask = np.all(~self.boa.mask,axis = 0 ) &\
np.all(self.boa >= 0.001, axis = 0) &\
np.all(self.boa < 1, axis = 0)
toa_mask = (~self.bad_pixs) &\
np.all(self.toa >= 0.0001, axis = 0) &\
np.all(self.toa < 1., axis = 0)
self.l8_mask = boa_mask & toa_mask & qua_mask
self.Hx = self.Hx [self.l8_mask]
self.Hy = self.Hy [self.l8_mask]
self.toa = self.toa [:, self.l8_mask]
self.boa = self.boa [:, self.l8_mask]
self.boa_unc = self.boa_qa[:, self.l8_mask]
self.logger.info('Solving...')
tempm = np.zeros((self.efull_res, self.efull_res))
tempm[self.Hx, self.Hy] = 1
tempm = tempm.reshape(self.num_blocks, self.block_size, \
self.num_blocks, self.block_size).astype(int).sum(axis=(3,1))
self.mask = np.nansum(self._extend_vals((~self.dcloud).astype(int)).reshape(shape1), axis=(3,1))
self.mask = ((self.mask/((1.*self.block_size)**2)) > 0.) & ((tempm/((self.aero_res/500.)**2)) > 0.) & \
(np.any(~np.isnan([self.aot, self.tcwv, self.tco3, self.sza[0]]), axis = 0))
self.mask = binary_erosion(self.mask, structure=np.ones((5, 5)).astype(bool))
self.tcwv[~self.mask] = np.nanmean(self.tcwv)
self.tco3[~self.mask] = np.nanmean(self.tco3)
self.aero = solving_atmo_paras(self.boa,
self.toa,
self.sza,
self.vza,
self.saa,
self.vaa,
self.aot,
self.tcwv,
self.tco3,
self.ele,
self.aot_unc,
self.tcwv_unc,
self.tco3_unc,
self.boa_unc,
self.Hx, self.Hy,
self.mask,
(self.efull_res, self.efull_res),
self.aero_res,
self.emus,
self.band_indexs,
self.boa_bands,
gamma = 2.,
alpha = -1.2,
pix_res = 30)
solved = self.aero._optimization()
return solved
def _get_ddv_aot(self, toa, l8, tcwv, tco3, ele_data):
ndvi_mask = (((toa[3] - 0.5*toa[5])/(toa[3] + 0.5*toa[5])) > 0.5) & (toa[5] > 0.01) & (toa[5] < 0.25) & (~self.dcloud)
if ndvi_mask.sum() < 100:
self.logger.info('No enough DDV found in this sence for aot restieval, and only cams prediction used.')
else:
Hx, Hy = np.where(ndvi_mask)
if ndvi_mask.sum() > 1000:
random_choice = np.random.choice(len(Hx), 1000, replace=False)
random_choice.sort()
Hx, Hy = Hx[random_choice], Hy[random_choice]
ndvi_mask[:] = False
ndvi_mask[Hx, Hy] = True
Hx, Hy = np.where(ndvi_mask)
blue_vza = np.cos(np.deg2rad(self.vza[0, Hx, Hy]))
blue_sza = np.cos(np.deg2rad(self.sza[0, Hx, Hy]))
red_vza = np.cos(np.deg2rad(self.vza[2, Hx, Hy]))
red_sza = np.cos(np.deg2rad(self.sza[2, Hx, Hy]))
blue_raa = np.cos(np.deg2rad(self.vaa[0, Hx, Hy] - self.saa[0, Hx, Hy]))
red_raa = np.cos(np.deg2rad(self.vaa[2, Hx, Hy] - self.saa[2, Hx, Hy]))
red, blue = toa[2, Hx, Hy], toa[0, Hx, Hy]
swif = toa[5, Hx, Hy]
red_emus = np.array(self.emus)[:, 3]
blue_emus = np.array(self.emus)[:, 1]
zero_aod = np.zeros_like(red)
red_inputs = np.array([red_sza, red_vza, red_raa, zero_aod, tcwv[Hx, Hy], tco3[Hx, Hy], ele_data[Hx, Hy]])
blue_inputs = np.array([blue_sza, blue_vza, blue_raa, zero_aod, tcwv[Hx, Hy], tco3[Hx, Hy], ele_data[Hx, Hy]])
p = np.r_[np.arange(0, 1., 0.02), np.arange(1., 1.5, 0.05), np.arange(1.5, 2., 0.1)]
f = lambda aot: self._ddv_cost(aot, blue, red, swif, blue_inputs, red_inputs, blue_emus, red_emus)
costs = parmap(f, p)
min_ind = np.argmin(costs)
self.logger.info('DDV solved aod is %.02f, and it will used as the mean value for cams prediction.'% p[min_ind])
self.aot[:] = p[min_ind]
def _ddv_cost(self, aot, blue, red, swif, blue_inputs, red_inputs, blue_emus, red_emus):
blue_inputs[3, :] = aot
red_inputs [3, :] = aot
blue_xap_emu, blue_xbp_emu, blue_xcp_emu = blue_emus
red_xap_emu, red_xbp_emu, red_xcp_emu = red_emus
blue_xap, blue_xbp, blue_xcp = blue_xap_emu.predict(blue_inputs.T)[0], \
blue_xbp_emu.predict(blue_inputs.T)[0], \
blue_xcp_emu.predict(blue_inputs.T)[0]
red_xap, red_xbp, red_xcp = red_xap_emu.predict(red_inputs.T) [0], \
red_xbp_emu.predict(red_inputs.T) [0], \
red_xcp_emu.predict(red_inputs.T) [0]
y = blue_xap * blue - blue_xbp
blue_sur = y / (1 + blue_xcp * y)
y = red_xap * red - red_xbp
red_sur = y / (1 + red_xcp * y)
blue_dif = 0 #(blue_sur - 0.25 * swif)**2
red_dif = 0 #(red_sur - 0.5 * swif)**2
rb_dif = (blue_sur - 0.55 * red_sur)**2
cost = 0.5 * (blue_dif + red_dif + rb_dif)
return cost.sum()
def _read_cams(self, example_file, parameters = ['aod550', 'tcwv', 'gtco3'], this_scale=[1., 0.1, 46.698]):
netcdf_file = datetime.datetime(self.sen_time.year, self.sen_time.month, \
self.sen_time.day).strftime("%Y-%m-%d.nc")
template = 'NETCDF:"%s":%s'
ind = np.abs((self.sen_time.hour + self.sen_time.minute/60. + \
self.sen_time.second/3600.) - np.arange(0,25,3)).argmin()
sr = osr.SpatialReference()
sr.ImportFromEPSG(4326)
proj = sr.ExportToWkt()
results = []
for i, para in enumerate(parameters):
fname = template%(self.cams_dir + '/' + netcdf_file, para)
g = gdal.Open(fname)
g.SetProjection(proj)
sub = g.GetRasterBand(ind+1)
offset = sub.GetOffset()
scale = sub.GetScale()
bad_pix = int(sub.GetNoDataValue())
rep_g = reproject_data(g, example_file, outputType = gdal.GDT_Float32).g
data = rep_g.GetRasterBand(ind+1).ReadAsArray()
data = data*scale + offset
mask = (data == (bad_pix*scale + offset)) | np.isnan(data)
if mask.sum()>=1:
data[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), data[~mask])
results.append(data*this_scale[i])
return results
def solving_l8_aerosol(self,):
self.logger = logging.getLogger('Landsat 8 Atmospheric Correction')
self.logger.setLevel(logging.INFO)
if not self.logger.handlers:
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
self.logger.addHandler(ch)
self.logger.propagate = False
self.sensor = 'OLI'
self.logger.info('Doing Landsat 8 tile: (%s, %s) on %d-%02d-%02d.' \
% (self.l8_tile[0], self.l8_tile[1], self.year, self.month, self.day))
#try:
solved = np.array(self._l8_aerosol()[0])
self.solved = solved.reshape(3, self.num_blocks, self.num_blocks)
#except:
# pass
#self.solved = np.array([self.aot, self.tcwv, self.tco3])
self.logger.info('Finished retrieval and saving them into local files.')
g = gdal.Open(self.example_file)
xmin, ymax = g.GetGeoTransform()[0], g.GetGeoTransform()[3]
projection = g.GetProjection()
para_names = 'aot', 'tcwv', 'tco3'
priors = [self.aot, self.tcwv, self.tco3]
for i,para_map in enumerate(self.solved):
if self.mask.sum()>0:
g_data = griddata(np.array(np.where(self.mask)).T, para_map[self.mask], \
(np.repeat(range(self.num_blocks), self.num_blocks).reshape(self.num_blocks, self.num_blocks), \
np.tile (range(self.num_blocks), self.num_blocks).reshape(self.num_blocks, self.num_blocks)), method='nearest')
#s = smoothn(g_data.copy(), isrobust=True, verbose=False)[1]
#g_data = smoothn(g_data.copy(), isrobust=True, verbose=False, s=s)[0]
else:
g_data = priors[i]
self.solved[i] = g_data
xres, yres = self.block_size * 30, self.block_size * 30
geotransform = (xmin, xres, 0, ymax, 0, -yres)
nx, ny = self.num_blocks, self.num_blocks
outputFileName = self.l8_toa_dir + '/%s_%s.tif'%(self.l8_header, para_names[i])
if os.path.exists(outputFileName):
os.remove(outputFileName)
dst_ds = gdal.GetDriverByName('GTiff').Create(outputFileName, ny, nx, 1, gdal.GDT_Float32)
dst_ds.SetGeoTransform(geotransform)
dst_ds.SetProjection(projection)
dst_ds.GetRasterBand(1).WriteArray(g_data)
dst_ds.FlushCache()
dst_ds = None
self.aot_map, self.tcwv_map, self.tco3_map = self.solved
if __name__ == '__main__':
aero = solve_aerosol(2017, 7, 10, l8_tile = (123, 34), mcd43_dir = '/data/selene/ucfajlg/Hebei/MCD43/')
aero.solving_l8_aerosol()