def _modis_aerosol(self, ):
self.modis_logger.info('Getting emualated surface reflectance.')
vza, sza, vaa, saa = self.modis_angle
self.modis_boa, self.modis_boa_qa, self.brdf_stds = get_brdf_six(
self.mcd43_file,
angles=[vza, sza, vaa - saa],
bands=(1, 2, 3, 4, 5, 6, 7),
Linds=None)
if self.mod_cloud is None:
self.modis_cloud = np.zeros_like(self.modis_toa[0]).astype(bool)
self.modis_logger.info('Getting elevation.')
ele = reproject_data(self.global_dem, self.example_file)
ele.get_it()
mask = ~np.isfinite(ele.data)
ele.data = np.ma.array(ele.data, mask=mask)
self.elevation = ele.data / 1000.
self.modis_logger.info('Getting pripors from ECMWF forcasts.')
aod, tcwv, tco3 = self._read_cams(self.example_file)
self.aod550 = aod * (1 - 0.14) # validation of +14% biase
self.tco3 = tco3 * 46.698 * (1 - 0.05)
self.tcwv = tcwv / 10.
self.tco3_unc = np.ones(self.tco3.shape) * 0.2
self.aod550_unc = np.ones(self.aod550.shape) * 0.5
self.tcwv_unc = np.ones(self.tcwv.shape) * 0.2
qua_mask = np.all(self.modis_boa_qa <= self.qa_thresh, axis=0)
boa_mask = np.all(~self.modis_boa.mask, axis = 0) &\
np.all(self.modis_boa>0, axis=0) &\
np.all(self.modis_boa<1, axis=0)
toa_mask = np.all(np.isfinite(self.modis_toa), axis=0) &\
np.all(self.modis_toa>0, axis=0) & \
np.all(self.modis_toa<1, axis=0)
self.modis_mask = qua_mask & boa_mask & toa_mask & (~self.modis_cloud)
self.modis_AEE, self.modis_bounds = self._load_emus(self.modis_sensor)
self.modis_solved = []
def _s2_aerosol(self, ):
self.s2_logger.propagate = False
self.s2_logger.info('Start to retrieve atmospheric parameters.')
self.s2 = read_s2(self.s2_toa_dir, self.s2_tile, self.year, self.month,
self.day, self.s2_u_bands)
self.s2_logger.info('Reading in TOA reflectance.')
selected_img = self.s2.get_s2_toa()
self.s2_file_dir = self.s2.s2_file_dir
self.s2.get_s2_cloud()
self.s2_logger.info(
'Find corresponding pixels between S2 and MODIS tiles')
tiles = Find_corresponding_pixels(self.s2.s2_file_dir + '/B04.jp2',
destination_res=500)
if len(tiles.keys()) > 1:
self.s2_logger.info('This sentinel 2 tile covers %d MODIS tile.' %
len(tiles.keys()))
self.mcd43_files = []
szas, vzas, saas, vaas, raas = [], [], [], [], []
boas, boa_qas, brdf_stds, Hxs, Hys = [], [], [], [], []
for key in tiles.keys():
#h,v = int(key[1:3]), int(key[-2:])
self.s2_logger.info('Getting BOA from MODIS tile: %s.' % key)
mcd43_file = glob(self.mcd43_tmp %
(self.mcd43_dir, self.year, self.doy, key))[0]
self.mcd43_files.append(mcd43_file)
self.H_inds, self.L_inds = tiles[key]
Lx, Ly = self.L_inds
Hx, Hy = self.H_inds
Hxs.append(Hx)
Hys.append(Hy)
self.s2_logger.info(
'Getting the angles and simulated surface reflectance.')
self.s2.get_s2_angles(self.reconstruct_s2_angle)
self.s2_angles = np.zeros((4, 6, len(Hx)))
for j, band in enumerate(self.s2_u_bands[:-2]):
self.s2_angles[[0,2],j,:] = self.s2.angles['vza'][band][Hx, Hy], \
self.s2.angles['vaa'][band][Hx, Hy]
self.s2_angles[[1,3],j,:] = self.s2.angles['sza'][Hx, Hy], \
self.s2.angles['saa'][Hx, Hy]
#use mean value to fill bad values
for i in range(4):
mask = ~np.isfinite(self.s2_angles[i])
if mask.sum() > 0:
self.s2_angles[i][mask] = np.interp(np.flatnonzero(mask), \
np.flatnonzero(~mask), self.s2_angles[i][~mask]) # simple interpolation
vza, sza = self.s2_angles[:2]
vaa, saa = self.s2_angles[2:]
raa = vaa - saa
szas.append(sza)
vzas.append(vza)
raas.append(raa)
vaas.append(vaa)
saas.append(saa)
# get the simulated surface reflectance
s2_boa, s2_boa_qa, brdf_std = get_brdf_six(mcd43_file, angles=[vza, sza, raa],\
bands=(3,4,1,2,6,7), Linds= [Lx, Ly])
boas.append(s2_boa)
boa_qas.append(s2_boa_qa)
brdf_stds.append(brdf_std)
self.s2_boa = np.hstack(boas)
self.s2_boa_qa = np.hstack(boa_qas)
self.brdf_stds = np.hstack(brdf_stds)
self.Hx = np.hstack(Hxs)
self.Hy = np.hstack(Hys)
vza = np.hstack(vzas)
sza = np.hstack(szas)
vaa = np.hstack(vaas)
saa = np.hstack(saas)
raa = np.hstack(raas)
self.s2_angles = np.array([vza, sza, vaa, saa])
#self.s2_boa, self.s2_boa_qa = self.s2_boa.flatten(), self.s2_boa_qa.flatten()
self.s2_logger.info('Applying spectral transform.')
self.s2_boa = self.s2_boa*np.array(self.s2_spectral_transform)[0,:-1][...,None] + \
np.array(self.s2_spectral_transform)[1,:-1][...,None]
self.s2_logger.info('Getting elevation.')
ele_data = reproject_data(self.global_dem,
self.s2.s2_file_dir + '/B04.jp2',
outputType=gdal.GDT_Float32).data
mask = ~np.isfinite(ele_data)
ele_data = np.ma.array(ele_data, mask=mask) / 1000.
self.elevation = ele_data[self.Hx, self.Hy]
self.s2_logger.info('Getting pripors from ECMWF forcasts.')
sen_time_str = json.load(
open(self.s2.s2_file_dir + '/tileInfo.json', 'r'))['timestamp']
self.sen_time = datetime.datetime.strptime(sen_time_str,
u'%Y-%m-%dT%H:%M:%S.%fZ')
example_file = self.s2.s2_file_dir + '/B04.jp2'
aod, tcwv, tco3 = np.array(self._read_cams(example_file))[:, self.Hx,
self.Hy]
self.s2_aod550 = aod #* (1-0.14) # validation of +14% biase
self.s2_tco3 = tco3 * 46.698 #* (1 - 0.05)
tcwv = tcwv / 10.
self.s2_tco3_unc = np.ones(self.s2_tco3.shape) * 0.2
self.s2_aod550_unc = np.ones(self.s2_aod550.shape) * 0.5
self.s2_logger.info(
'Trying to get the tcwv from the emulation of sen2cor look up table.'
)
#try:
self._get_tcwv(selected_img, vza, sza, raa, ele_data)
#except:
# self.s2_logger.warning('Getting tcwv from the emulation of sen2cor look up table failed, ECMWF data used.')
# self.s2_tcwv = tcwv
# self.s2_tcwv_unc = np.ones(self.s2_tcwv.shape) * 0.2
self.s2_logger.info('Trying to get the aod from ddv method.')
try:
solved = self._get_ddv_aot(self.s2.angles, example_file,
self.s2_tcwv, ele_data, selected_img)
if solved[0] < 0:
self.s2_logger.warning(
'DDV failed and only cams data used for the prior.')
else:
self.s2_logger.info(
'DDV solved aod is %.02f, and it will used as the mean value of cams prediction.'
% solved[0])
self.s2_aod550 += (solved[0] - self.s2_aod550.mean())
except:
self.s2_logger.warning('Getting aod from ddv failed.')
self.s2_logger.info('Applying PSF model.')
if self.s2_psf is None:
self.s2_logger.info('No PSF parameters specified, start solving.')
high_img = np.repeat(
np.repeat(selected_img['B11'], 2, axis=0), 2, axis=1) * 0.0001
high_indexs = self.Hx, self.Hy
low_img = self.s2_boa[4]
qa, cloud = self.s2_boa_qa[4], self.s2.cloud
psf = psf_optimize(high_img, high_indexs, low_img, qa, cloud, 2)
xs, ys = psf.fire_shift_optimize()
xstd, ystd = 29.75, 39
ang = 0
self.s2_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()))
else:
xstd, ystd, ang, xs, ys = self.s2_psf
# apply psf shifts without going out of the image extend
shifted_mask = np.logical_and.reduce(
((self.Hx + int(xs) >= 0),
(self.Hx + int(xs) < self.s2_full_res[0]),
(self.Hy + int(ys) >= 0),
(self.Hy + int(ys) < self.s2_full_res[0])))
self.Hx, self.Hy = self.Hx[shifted_mask] + int(
xs), self.Hy[shifted_mask] + int(ys)
#self.Lx, self.Ly = self.Lx[shifted_mask], self.Ly[shifted_mask]
self.s2_boa = self.s2_boa[:, shifted_mask]
self.s2_boa_qa = self.s2_boa_qa[:, shifted_mask]
self.s2_angles = self.s2_angles[:, :, shifted_mask]
self.elevation = self.elevation[shifted_mask]
self.s2_aod550 = self.s2_aod550[shifted_mask]
self.s2_tcwv = self.s2_tcwv[shifted_mask]
self.s2_tco3 = self.s2_tco3[shifted_mask]
self.s2_aod550_unc = self.s2_aod550_unc[shifted_mask]
self.s2_tcwv_unc = self.s2_tcwv_unc[shifted_mask]
self.s2_tco3_unc = self.s2_tco3_unc[shifted_mask]
self.brdf_stds = self.brdf_stds[:, shifted_mask]
self.s2_logger.info('Getting the convolved TOA reflectance.')
self.valid_pixs = sum(
shifted_mask) # count how many pixels is still within the s2 tile
ker_size = 2 * int(round(max(1.96 * xstd, 1.96 * ystd)))
self.bad_pixs = np.zeros(self.valid_pixs).astype(bool)
imgs = []
for i, band in enumerate(self.s2_u_bands[:-2]):
if selected_img[band].shape != self.s2_full_res:
selected_img[band] = self.repeat_extend(selected_img[band],
shape=self.s2_full_res)
else:
pass
selected_img[band][0, :] = -9999
selected_img[band][-1, :] = -9999
selected_img[band][:, 0] = -9999
selected_img[band][:, -1] = -9999
imgs.append(selected_img[band])
# filter out the bad pixels
self.bad_pixs |= cloud_dilation(self.s2.cloud |\
(selected_img[band] <= 0) | \
(selected_img[band] >= 10000),\
iteration= ker_size/2)[self.Hx, self.Hy]
del selected_img
del self.s2.selected_img
del high_img
del self.s2.angles
del self.s2.sza
del self.s2.saa
del self.s2
ker = self.gaussian(xstd, ystd, ang)
f = lambda img: signal.fftconvolve(img, ker, mode='same')[self.Hx, self
.Hy] * 0.0001
half = parmap(f, imgs[:3])
self.s2_toa = np.array(half + parmap(f, imgs[3:]))
#self.s2_toa = np.array(parmap(f,imgs))
del imgs
# get the valid value masks
qua_mask = np.all(self.s2_boa_qa <= self.qa_thresh, axis=0)
boa_mask = np.all(~self.s2_boa.mask,axis = 0 ) &\
np.all(self.s2_boa > 0, axis = 0) &\
np.all(self.s2_boa < 1, axis = 0)
toa_mask = (~self.bad_pixs) &\
np.all(self.s2_toa > 0, axis = 0) &\
np.all(self.s2_toa < 1, axis = 0)
self.s2_mask = boa_mask & toa_mask & qua_mask & (~self.elevation.mask)
self.s2_AEE, self.s2_bounds = self._load_emus(self.s2_sensor)
def _s2_aerosol(self, ):
self.s2_logger.propagate = False
self.s2_logger.info('Start to retrieve atmospheric parameters.')
self.s2 = read_s2(self.s2_toa_dir, self.s2_tile, self.year, self.month,
self.day, self.s2_u_bands)
self.s2_logger.info('Reading in TOA reflectance.')
selected_img = self.s2.get_s2_toa()
self.s2_file_dir = self.s2.s2_file_dir
self.s2.get_s2_cloud()
self.s2_logger.info('Loading emulators.')
self._load_xa_xb_xc_emus()
self.s2_logger.info(
'Find corresponding pixels between S2 and MODIS tiles')
tiles = Find_corresponding_pixels(self.s2.s2_file_dir + '/B04.jp2',
destination_res=500)
if len(tiles.keys()) > 1:
self.s2_logger.info('This sentinel 2 tile covers %d MODIS tile.' %
len(tiles.keys()))
self.mcd43_files = []
boas, boa_qas, brdf_stds, Hxs, Hys = [], [], [], [], []
self.s2_logger.info(
'Getting the angles and simulated surface reflectance.')
for key in tiles.keys():
self.s2_logger.info('Getting BOA from MODIS tile: %s.' % key)
mcd43_file = glob(self.mcd43_tmp %
(self.mcd43_dir, self.year, self.doy, key))[0]
self.mcd43_files.append(mcd43_file)
self.H_inds, self.L_inds = tiles[key]
Lx, Ly = self.L_inds
Hx, Hy = self.H_inds
Hxs.append(Hx)
Hys.append(Hy)
self.s2.get_s2_angles(self.reconstruct_s2_angle)
self.s2_angles = np.zeros((4, 6, len(Hx)))
for j, band in enumerate(self.s2_u_bands[:-2]):
self.s2_angles[[0,2],j,:] = (self.s2.angles['vza'][band])[Hx, Hy], \
(self.s2.angles['vaa'][band])[Hx, Hy]
self.s2_angles[[1,3],j,:] = self.s2.angles['sza'][Hx, Hy], \
self.s2.angles['saa'][Hx, Hy]
#use mean value to fill bad values
for i in range(4):
mask = ~np.isfinite(self.s2_angles[i])
if mask.sum() > 0:
self.s2_angles[i][mask] = np.interp(np.flatnonzero(mask), \
np.flatnonzero(~mask), \
self.s2_angles[i][~mask]) # simple interpolation
vza, sza = self.s2_angles[:2]
vaa, saa = self.s2_angles[2:]
raa = vaa - saa
# get the simulated surface reflectance
s2_boa, s2_boa_qa, brdf_std = get_brdf_six(mcd43_file, angles=[vza, sza, raa],\
bands=(3,4,1,2,6,7), Linds= [Lx, Ly])
boas.append(s2_boa)
boa_qas.append(s2_boa_qa)
brdf_stds.append(brdf_std)
self.s2_boa = np.hstack(boas)
self.s2_boa_qa = np.hstack(boa_qas)
self.brdf_stds = np.hstack(brdf_stds)
self.s2_logger.info('Applying spectral transform.')
self.s2_boa = self.s2_boa*np.array(self.s2_spectral_transform)[0,:-1][...,None] + \
np.array(self.s2_spectral_transform)[1,:-1][...,None]
self.Hx = np.hstack(Hxs)
self.Hy = np.hstack(Hys)
del sza
del vza
del saa
del vaa
del raa
del mask
del boas
del boa_qas
del brdf_stds
del Hxs
del Hys
shape = (self.num_blocks, self.s2.angles['sza'].shape[0] / self.num_blocks, \
self.num_blocks, self.s2.angles['sza'].shape[1] / self.num_blocks)
self.sza = self.s2.angles['sza'].reshape(shape).mean(axis=(3, 1))
self.saa = self.s2.angles['saa'].reshape(shape).mean(axis=(3, 1))
self.vza = []
self.vaa = []
for band in self.s2_u_bands[:-2]:
self.vza.append(
self.s2.angles['vza'][band].reshape(shape).mean(axis=(3, 1)))
self.vaa.append(
self.s2.angles['vaa'][band].reshape(shape).mean(axis=(3, 1)))
self.vza = np.array(self.vza)
self.vaa = np.array(self.vaa)
self.raa = self.saa[None, ...] - self.vaa
self.s2_logger.info('Getting elevation.')
example_file = self.s2.s2_file_dir + '/B04.jp2'
ele_data = reproject_data(self.global_dem,
example_file,
outputType=gdal.GDT_Float32).data
mask = ~np.isfinite(ele_data)
ele_data = np.ma.array(ele_data, mask=mask) / 1000.
self.elevation = ele_data.reshape((self.num_blocks, ele_data.shape[0] / self.num_blocks, \
self.num_blocks, ele_data.shape[1] / self.num_blocks)).mean(axis=(3,1))
self.s2_logger.info('Getting pripors from ECMWF forcasts.')
sen_time_str = json.load(
open(self.s2.s2_file_dir + '/tileInfo.json', 'r'))['timestamp']
self.sen_time = datetime.datetime.strptime(sen_time_str,
u'%Y-%m-%dT%H:%M:%S.%fZ')
aot, tcwv, tco3 = np.array(self._read_cams(example_file)).reshape((3, self.num_blocks, \
self.block_size, self.num_blocks, self.block_size)).mean(axis=(4, 2))
self.aot = aot #* (1-0.14) # validation of +14% biase
self.tco3 = tco3 * 46.698 #* (1 - 0.05)
tcwv = tcwv / 10.
self.tco3_unc = np.ones(self.tco3.shape) * 0.2
self.aot_unc = np.ones(self.aot.shape) * 0.5
self.s2_logger.info(
'Trying to get the tcwv from the emulation of sen2cor look up table.'
)
try:
self._get_tcwv(selected_img, self.s2.angles['vza'],
self.s2.angles['vaa'], self.s2.angles['sza'],
self.s2.angles['saa'], ele_data)
except:
self.s2_logger.warning(
'Getting tcwv from the emulation of sen2cor look up table failed, ECMWF data used.'
)
self.tcwv = tcwv
self.tcwv_unc = np.ones(self.tcwv.shape) * 0.2
self.s2_logger.info('Trying to get the aot from ddv method.')
try:
solved = self._get_ddv_aot(selected_img)
if solved[0] < 0:
self.s2_logger.warning(
'DDV failed and only cams data used for the prior.')
else:
self.s2_logger.info(
'DDV solved aot is %.02f, and it will used as the mean value of cams prediction.'
% solved[0])
self.aot += (solved[0] - self.aot.mean())
except:
self.s2_logger.warning('Getting aot from ddv failed.')
self.s2_logger.info('Applying PSF model.')
if self.s2_psf is None:
xstd, ystd, ang, xs, ys = self._get_psf(selected_img)
else:
xstd, ystd, ang, xs, ys = self.s2_psf
# apply psf shifts without going out of the image extend
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[0])))
self.Hx, self.Hy = self.Hx[shifted_mask] + int(
xs), self.Hy[shifted_mask] + int(ys)
#self.Lx, self.Ly = self.Lx[shifted_mask], self.Ly[shifted_mask]
self.s2_boa = self.s2_boa[:, shifted_mask]
self.s2_boa_qa = self.s2_boa_qa[:, shifted_mask]
self.brdf_stds = self.brdf_stds[:, shifted_mask]
self.s2_logger.info('Getting the convolved TOA reflectance.')
self.valid_pixs = sum(
shifted_mask) # count how many pixels is still within the s2 tile
ker_size = 2 * int(round(max(1.96 * xstd, 1.96 * ystd)))
self.bad_pixs = np.zeros(self.valid_pixs).astype(bool)
imgs = []
for i, band in enumerate(self.s2_u_bands[:-2]):
if selected_img[band].shape != self.full_res:
imgs.append(
self.repeat_extend(selected_img[band],
shape=self.full_res))
else:
imgs.append(selected_img[band])
border_mask = np.zeros(self.full_res).astype(bool)
border_mask[[0, -1], :] = True
border_mask[:, [0, -1]] = True
self.bad_pixs = cloud_dilation(self.s2.cloud | border_mask,
iteration=ker_size / 2)[self.Hx,
self.Hy]
del selected_img
del self.s2.selected_img
del self.s2.angles['vza']
del self.s2.angles['vaa']
del self.s2.angles['sza']
del self.s2.angles['saa']
del self.s2.sza
del self.s2.saa
del self.s2
ker = self.gaussian(xstd, ystd, ang)
f = lambda img: signal.fftconvolve(img, ker, mode='same')[self.Hx, self
.Hy] * 0.0001
half = parmap(f, imgs[:3])
self.s2_toa = np.array(half + parmap(f, imgs[3:]))
del imgs
# get the valid value masks
qua_mask = np.all(self.s2_boa_qa <= self.qa_thresh, axis=0)
boa_mask = np.all(~self.s2_boa.mask,axis = 0 ) &\
np.all(self.s2_boa > 0, axis = 0) &\
np.all(self.s2_boa < 1, axis = 0)
toa_mask = (~self.bad_pixs) &\
np.all(self.s2_toa > 0, axis = 0) &\
np.all(self.s2_toa < 1, axis = 0)
self.s2_mask = boa_mask & toa_mask & qua_mask
self.Hx = self.Hx[self.s2_mask]
self.Hy = self.Hy[self.s2_mask]
self.s2_toa = self.s2_toa[:, self.s2_mask]
self.s2_boa = self.s2_boa[:, self.s2_mask]
self.s2_boa_qa = self.s2_boa_qa[:, self.s2_mask]
self.brdf_stds = self.brdf_stds[:, self.s2_mask]
self.s2_boa_unc = grab_uncertainty(self.s2_boa, self.boa_bands,
self.s2_boa_qa,
self.brdf_stds).get_boa_unc()
self.s2_logger.info('Solving...')
self.aero = solving_atmo_paras(
self.s2_boa, self.s2_toa, self.sza, self.vza, self.saa, self.vaa,
self.aot, self.tcwv, self.tco3, self.elevation, self.aot_unc,
self.tcwv_unc, self.tco3_unc, self.s2_boa_unc, self.Hx, self.Hy,
self.full_res, self.aero_res, self.emus, self.band_indexs,
self.boa_bands)
solved = self.aero._optimization()
return solved
def S2_PSF_optimization(self,):
# open the created vrt file with 10 meter, 20 meter and 60 meter
# grouped togehter and use gdal memory map to open it
g = gdal.Open(self.s2_dir+'10meter.vrt')
data= g.GetVirtualMemArray()
b2,b3,b4,b8 = data
g1 = gdal.Open(self.s2_dir+'20meter.vrt')
data1 = g1.GetVirtualMemArray()
b8a, b11, b12 = data1[-3:,:,:]
img = dict(zip(self.bands, [b2,b3,b4,b8, b11, b12, b8a]))
if glob(self.s2_dir+'cloud.tiff')==[]:
cl = classification(img = img)
cl.Get_cm_p()
g=None; g1=None
self.cloud = cl.cm
g = gdal.Open(self.s2_dir+'B04.jp2')
driver = gdal.GetDriverByName('GTiff')
g1 = driver.Create(self.s2_dir+'cloud.tiff', g.RasterXSize, g.RasterYSize, 1, gdal.GDT_Byte)
projection = g.GetProjection()
geotransform = g.GetGeoTransform()
g1.SetGeoTransform( geotransform )
g1.SetProjection( projection )
gcp_count = g.GetGCPs()
if gcp_count != 0:
g1.SetGCPs( gcp_count, g.GetGCPProjection() )
g1.GetRasterBand(1).WriteArray(self.cloud)
g1=None; g=None
del cl
else:
self.cloud = cloud = gdal.Open(self.s2_dir+'cloud.tiff').ReadAsArray().astype(bool)
cloud_cover = 1.*self.cloud.sum()/self.cloud.size
cloud_cover = 1.*self.cloud.sum()/self.cloud.size
if cloud_cover > 0.2:
print 'Too much cloud, cloud proportion: %.03f !!'%cloud_cover
return []
else:
mete = readxml('%smetadata.xml'%self.s2_dir)
self.sza = np.zeros(7)
self.sza[:] = mete['mSz']
self.saa = self.sza.copy()
self.saa[:] = mete['mSa']
try:
self.vza = (mete['mVz'])[[1,2,3,7,11,12,8],]
self.vaa = (mete['mVa'])[[1,2,3,7,11,12,8],]
except:
self.vza = np.repeat(np.nanmean(mete['mVz']), 7)
self.vaa = np.repeat(np.nanmean(mete['mVa']), 7)
self.angles = (self.sza, self.vza, (self.vaa - self.saa))
tiles = Find_corresponding_pixels(self.s2_dir+'B04.jp2', destination_res=500)
self.h,self.v = int(self.Lfile.split('.')[-4][1:3]), int(self.Lfile.split('.')[-4][4:])
self.H_inds, self.L_inds = tiles['h%02dv%02d'%(self.h, self.v)]
self.Lx, self.Ly = self.L_inds
self.Hx, self.Hy = self.H_inds
angles = (self.sza[-2], self.vza[-2], (self.vaa - self.saa)[-2])
self.brdf, self.qa = get_brdf_six(self.Lfile, angles=angles, bands=(7,), Linds=list(self.L_inds))
self.brdf, self.qa = self.brdf.flatten(), self.qa.flatten()
# convolve band 12 using the generally used PSF value
self.H_data = np.repeat(np.repeat(b12, 2, axis=1), 2, axis=0)
size = 2*int(round(max(1.96*50, 1.96*50)))# set the largest possible PSF size
self.H_data[0,:]=self.H_data[-1,:]=self.H_data[:,0]=self.H_data[:,-1]=0
self.bad_pixs = cloud_dilation( (self.H_data <= 0) | self.cloud , iteration=size/2)
xstd, ystd = 29.75, 39
ker = self.gaussian(xstd, ystd, 0)
self.conved = signal.fftconvolve(self.H_data, ker, mode='same')
m_mask = np.all(~self.brdf.mask,axis=0 ) & np.all(self.qa<=self.qa_thresh, axis=0)
s_mask = ~self.bad_pixs[self.Hx, self.Hy]
self.ms_mask = s_mask & m_mask
'''self.in_patch_m = np.logical_and.reduce(((self.Hx>=self.patch[1]),
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)
l8._get_angles()
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)
tiles = Find_corresponding_pixels(self.example_file,
destination_res=500)
if len(tiles.keys()) > 1:
self.logger.info('This Landsat 8 tile covers %d MODIS tile.' %
len(tiles.keys()))
self.mcd43_files = []
boas, boa_qas, brdf_stds, Hxs, Hys = [], [], [], [], []
for key in tiles.keys()[1:]:
self.logger.info('Getting BOA from MODIS tile: %s.' % key)
mcd43_file = glob(self.mcd43_tmp %
(self.mcd43_dir, self.year, self.doy, key))[0]
self.mcd43_files.append(mcd43_file)
self.H_inds, self.L_inds = tiles[key]
Lx, Ly = self.L_inds
Hx, Hy = self.H_inds
Hxs.append(Hx)
Hys.append(Hy)
vza, sza = l8.vza[:, Hx, Hy], l8.sza[:, Hx, Hy]
vaa, saa = l8.vaa[:, Hx, Hy], l8.saa[:, Hx, Hy]
raa = vaa - saa
boa, boa_qa, brdf_std = get_brdf_six(mcd43_file, angles=[vza, sza, raa],\
bands=(3,4,1,2,6,7), Linds= [Lx, Ly])
boas.append(boa)
boa_qas.append(boa_qa)
brdf_stds.append(brdf_std)
self.boa = np.hstack(boas)
self.boa_qa = np.hstack(boa_qas)
self.brdf_stds = np.hstack(brdf_stds)
self.logger.info('Applying spectral transform.')
self.boa = self.boa*np.array(self.spectral_transform)[0][...,None] + \
np.array(self.spectral_transform)[1][...,None]
self.Hx = np.hstack(Hxs)
self.Hy = np.hstack(Hys)
self.sza = l8.sza[:, self.Hx, self.Hy]
self.vza = l8.vza[:, self.Hx, self.Hy]
self.saa = l8.saa[:, self.Hx, self.Hy]
self.vaa = l8.vaa[:, self.Hx, self.Hy]
self.logger.info('Reading in TOA reflectance.')
toa = l8._get_toa()
self.toa = toa[:, self.Hx, self.Hy]
self.sen_time = l8.sen_time
self.logger.info('Getting elevation.')
ele_data = reproject_data(self.global_dem, self.example_file).data
mask = ~np.isfinite(ele_data)
ele_data = np.ma.array(ele_data, mask=mask) / 1000.
self.logger.info('Getting pripors from ECMWF forcasts.')
aot, tcwv, tco3 = np.array(self._read_cams(self.example_file))
self._get_ddv_aot(toa, l8, tcwv, tco3, ele_data)
aot, tcwv, tco3 = np.array(self._read_cams(self.example_file))
self.aot = aot[self.Hx,
self.Hy] #* (1-0.14) # validation of +14% biase
self.tco3 = tco3[self.Hx, self.Hy] #* (1 - 0.05)
self.tcwv = tcwv[self.Hx, self.Hy]
self.aot_unc = np.ones(self.aot.shape) * 0.5
self.tcwv_unc = np.ones(self.tcwv.shape) * 0.2
self.tco3_unc = np.ones(self.tco3.shape) * 0.2