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)
f_dat[mask] = np.nan
unc_dat[mask] = np.nan
return f_dat, unc_dat
if __name__ == '__main__':
example_file = '/store/S2_data/50/S/LH/2017/2/24/0/B04.jp2'
mcd43_dir = '/data/selene/ucfajlg/Hebei/MCD43/'
bands = [3, 4, 1, 2, 6, 7]
va_files = [
'/'.join(example_file.split('/')[:-1]) +
'/angles/VAA_VZA_B%02d.img' % i for i in [2, 3, 4, 8, 11, 12]
]
sza = np.zeros((10980, 10980))
saa = sza.copy()
from grab_s2_toa import read_s2
s2 = read_s2('/store/S2_data/',
'50SLH',
2017,
2,
24,
bands=['B02', 'B03', 'B04', 'B08', 'B11', 'B12'])
s2.get_s2_angles()
sa_files = [s2.angles['saa'], s2.angles['sza']]
f_data, unc_data = MCD43_SurRef(mcd43_dir,
example_file,
2017,
55, [va_files, sa_files],
sun_view_ang_scale=[1., 0.01],
bands=bands)
def atmospheric_correction(self, ):
self.logger.propagate = False
self.s2_sensor = 'msi'
self.logger.info('Loading emulators.')
#self.s2_inv_AEE = self._load_inverse_emus(self.s2_sensor)
self._load_xa_xb_xc_emus()
self.s2 = read_s2(self.s2_toa_dir, self.s2_tile, \
self.year, self.month, self.day, bands=None)
self.logger.info('Reading in the reflectance.')
all_refs = self.s2.get_s2_toa()
self.logger.info('Reading in the angles')
self.s2.get_s2_angles(self.reconstruct_s2_angle)
self.sza, self.saa = self.s2.angles['sza'], self.s2.angles['saa']
self.logger.info('Doing 10 meter bands')
self._10meter_ref = np.array([all_refs[band].astype(float)/10000. for band \
in ['B02', 'B03', 'B04', 'B08']])
self._10meter_vza = np.array([
self.s2.angles['vza'][band]
for band in ['B02', 'B03', 'B04', 'B08']
])
self._10meter_vaa = np.array([
self.s2.angles['vaa'][band]
for band in ['B02', 'B03', 'B04', 'B08']
])
self.logger.info('Getting control variables for 10 meters bands.')
self._10meter_aod, self._10meter_tcwv, self._10meter_tco3,\
self._10meter_ele = self.get_control_variables('B04')
self._block_size = 3660
self._num_blocks = 10980 / self._block_size
self._mean_size = 60
self._10meter_band_indexs = [1, 2, 3, 7]
self.rsr = [PredefinedWavelengths.S2A_MSI_02, PredefinedWavelengths.S2A_MSI_03, \
PredefinedWavelengths.S2A_MSI_04, PredefinedWavelengths.S2A_MSI_08 ]
self.logger.info('Fire correction and splited into %d blocks.' %
self._num_blocks**2)
self.fire_correction(self._10meter_ref, self.sza, self._10meter_vza,\
self.saa, self._10meter_vaa, self._10meter_aod,\
self._10meter_tcwv, self._10meter_tco3, self._10meter_ele,\
self._10meter_band_indexs)
self.toa_rgb = clip(
self._10meter_ref[[2, 1, 0], ...].transpose(1, 2, 0) * 255 / 0.255,
0., 255.).astype(uint8)
self.boa_rgb = clip(
self.boa[[2, 1, 0], ...].transpose(1, 2, 0) * 255 / 0.255, 0.,
255.).astype(uint8)
self._save_rgb(self.toa_rgb, 'TOA_RGB.tif',
self.s2.s2_file_dir + '/B04.jp2')
self._save_rgb(self.boa_rgb, 'BOA_RGB.tif',
self.s2.s2_file_dir + '/B04.jp2')
del self._10meter_ref
del self._10meter_vza
del self._10meter_vaa
del self._10meter_aod \
del self._10meter_tcwv
del self._10meter_tco3
del self._10meter_ele
#self.sur_refs.update(dict(zip(['B02', 'B03', 'B04', 'B08'], self.boa)))
self._save_img(self.boa, ['B02', 'B03', 'B04', 'B08'])
del self.boa
self.logger.info('Doing 20 meter bands')
self._20meter_ref = np.array([all_refs[band].astype(float)/10000. for band \
in ['B05', 'B06', 'B07', 'B8A', 'B11', 'B12']])
self._20meter_vza = np.array([self.s2.angles['vza'][band] for band \
in ['B05', 'B06', 'B07', 'B8A', 'B11', 'B12']]).reshape(6, 5490, 2, 5490, 2).mean(axis=(4,2))
self._20meter_vaa = np.array([self.s2.angles['vaa'][band] for band \
in ['B05', 'B06', 'B07', 'B8A', 'B11', 'B12']]).reshape(6, 5490, 2, 5490, 2).mean(axis=(4,2))
self._20meter_sza = self.sza.reshape(5490, 2, 5490,
2).mean(axis=(3, 1))
self._20meter_saa = self.saa.reshape(5490, 2, 5490,
2).mean(axis=(3, 1))
self.logger.info('Getting control variables for 20 meters bands.')
self._20meter_aod, self._20meter_tcwv, self._20meter_tco3,\
self._20meter_ele = self.get_control_variables('B05')
self._block_size = 1830
self._num_blocks = 5490 / self._block_size
self._mean_size = 30
self._20meter_band_indexs = [4, 5, 6, 8, 11, 12]
self.rsr = [PredefinedWavelengths.S2A_MSI_05, PredefinedWavelengths.S2A_MSI_06, \
PredefinedWavelengths.S2A_MSI_07, PredefinedWavelengths.S2A_MSI_09, \
PredefinedWavelengths.S2A_MSI_12, PredefinedWavelengths.S2A_MSI_13]
self.logger.info('Fire correction and splited into %d blocks.' %
self._num_blocks**2)
self.fire_correction(self._20meter_ref, self._20meter_sza, self._20meter_vza,\
self._20meter_saa, self._20meter_vaa, self._20meter_aod,\
self._20meter_tcwv, self._20meter_tco3, self._20meter_ele,\
self._20meter_band_indexs)
del self._20meter_ref
del self._20meter_vza
del self._20meter_vaa
del self._20meter_sza
del self._20meter_saa
del self._20meter_aod
del self._20meter_tcwv
del self._20meter_tco3
del self._20meter_ele
#self.sur_refs.update(dict(zip(['B05', 'B06', 'B07', 'B8A', 'B11', 'B12'], self.boa)))
self._save_img(self.boa, ['B05', 'B06', 'B07', 'B8A', 'B11', 'B12'])
del self.boa
self.logger.info('Doing 60 meter bands')
self._60meter_ref = np.array([all_refs[band].astype(float)/10000. for band \
in ['B01', 'B09', 'B10']])
self._60meter_vza = np.array([self.s2.angles['vza'][band] for band \
in ['B01', 'B09', 'B10']]).reshape(3, 1830, 6, 1830, 6).mean(axis=(4, 2))
self._60meter_vaa = np.array([self.s2.angles['vaa'][band] for band \
in ['B01', 'B09', 'B10']]).reshape(3, 1830, 6, 1830, 6).mean(axis=(4, 2))
self._60meter_sza = self.sza.reshape(1830, 6, 1830,
6).mean(axis=(3, 1))
self._60meter_saa = self.saa.reshape(1830, 6, 1830,
6).mean(axis=(3, 1))
self.logger.info('Getting control variables for 60 meters bands.')
self._60meter_aod, self._60meter_tcwv, self._60meter_tco3,\
self._60meter_ele = self.get_control_variables('B09')
self._block_size = 610
self._num_blocks = 1830 / self._block_size
self._mean_size = 10
self._60meter_band_indexs = [0, 9, 10]
self.rsr = [
PredefinedWavelengths.S2A_MSI_01, PredefinedWavelengths.S2A_MSI_10,
PredefinedWavelengths.S2A_MSI_11
]
self.logger.info('Fire correction and splited into %d blocks.' %
self._num_blocks**2)
self.fire_correction(self._60meter_ref, self._60meter_sza, self._60meter_vza,\
self._60meter_saa, self._60meter_vaa, self._60meter_aod,\
self._60meter_tcwv, self._60meter_tco3, self._60meter_ele,\
self._60meter_band_indexs)
del self._60meter_ref
del self._60meter_vza
del self._60meter_vaa
del self._60meter_sza
del self._60meter_saa
del self._60meter_aod
del self._60meter_tcwv
del self._60meter_tco3
del self._60meter_ele
#self.sur_refs.update(dict(zip(['B01', 'B09', 'B10'], self.boa)))
self._save_img(self.boa, ['B01', 'B09', 'B10'])
del self.boa
del all_refs
del self.s2.selected_img
del self.s2.angles
self.logger.info('Done!')
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_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.example_file = self.s2.s2_file_dir + '/B04.jp2'
self.s2.get_s2_cloud()
self.s2_logger.info('Loading emulators.')
self._load_xa_xb_xc_emus()
self.s2_logger.info(
'Getting the angles and simulated surface reflectance.')
self.s2.get_s2_angles()
sa_files = [self.s2.angles['saa'], self.s2.angles['sza']]
if os.path.exists(self.s2_file_dir + '/angles/'):
va_files = [
self.s2_file_dir + '/angles/VAA_VZA_B%02d.img' % i
for i in [2, 3, 4, 8, 11, 12]
]
else:
va_files = [
np.array(
[self.s2.angles['vaa'][band], self.s2.angles['vza'][band]])
for band in self.s2_u_bands[:-2]
]
if len(glob(self.s2_file_dir + '/MCD43.npz')) == 0:
boa, unc, hx, hy, lx, ly, flist = MCD43_SurRef(self.mcd43_dir, self.example_file, \
self.year, self.doy, [sa_files, va_files],
sun_view_ang_scale=[1, 0.01], bands = [3,4,1,2,6,7], tolz=0.001, reproject=False)
np.savez(self.s2_file_dir + '/MCD43.npz',
boa=boa,
unc=unc,
hx=hx,
hy=hy,
lx=lx,
ly=ly,
flist=flist)
else:
f = np.load(self.s2_file_dir + '/MCD43.npz')
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.s2_logger.info('Update cloud mask.')
flist = [
f.split('/')[0] + self.mcd43_dir + '/' + f.split('/')[-1]
for f in flist
]
self._mcd43_cloud(flist, lx, ly, self.example_file, boa,
selected_img['B12'])
self.s2_logger.info('Applying spectral transform.')
self.s2_boa_qa = np.ma.array(unc)
self.s2_boa = np.ma.array(boa)*np.array(self.s2_spectral_transform)[0,:-1][...,None] + \
np.array(self.s2_spectral_transform)[1,:-1][...,None]
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.')
ele_data = reproject_data(self.global_dem,
self.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(self.example_file)).reshape((3, self.num_blocks, \
self.block_size, self.num_blocks, self.block_size)).mean(axis=(4, 2))
mod08_aot = self._mod08_aot()
#if np.isnan(mod08_aot):
self.aot = aot - (aot.mean() - np.nanmean([mod08_aot, aot.mean()]))
#else:
# temp = np.zeros_like(aot)
# temp[:] = mod08_aot
# self.aot = temp
self.tco3 = tco3 * 46.698
tcwv = tcwv / 10.
self.tco3_unc = np.ones(self.tco3.shape) * 0.2
self.aot_unc = np.ones(self.aot.shape) * 2.
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 + (solved[0]-self.aot.mean())
except:
self.s2_logger.warning('Getting aot from ddv failed.')
'''
self.s2_logger.info(
'Mean values for priors are: %.02f, %.02f, %.02f and mod08 aot is %.02f'
% (aot.mean(), self.tcwv.mean(), self.tco3.mean(), mod08_aot))
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.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.dcloud = binary_dilation(self.cloud | border_mask,
structure=np.ones((3, 3)).astype(bool),
iterations=ker_size / 2).astype(bool)
self.bad_pixs = self.dcloud[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 >= 0.001, axis = 0) &\
np.all(self.s2_boa < 1, axis = 0)
toa_mask = (~self.bad_pixs) &\
np.all(self.s2_toa >= 0.0001, 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_unc = self.s2_boa_qa[:, self.s2_mask]
self.s2_logger.info('Solving...')
tempm = np.zeros(self.full_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))
mask = ~self.dcloud
self.mask = mask.reshape(self.num_blocks, self.block_size, \
self.num_blocks, self.block_size).astype(int).sum(axis=(3,1))
self.mask = ((self.mask / ((1. * self.block_size)**2)) >= 0.5) & (
(tempm / ((self.aero_res / 500.)**2)) >= 0.5)
self.mask = binary_erosion(self.mask,
structure=np.ones((3, 3)).astype(bool))
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.mask, self.full_res, self.aero_res, self.emus,
self.band_indexs, self.boa_bands)
solved = self.aero._optimization()
return solved
from datetime import datetime
example_file = '/store/S2_data/50/S/MG/2016/1/24/0/B04.jp2'
date = datetime.strptime('/'.join(example_file.split('/')[-5:-2]),
'%Y/%m/%d')
doy = date.timetuple().tm_yday
mcd43_dir = '/data/selene/ucfajlg/Hebei/MCD43/'
bands = [3, 4, 1, 2, 6, 7]
va_files = [
'/'.join(example_file.split('/')[:-1]) +
'/angles/VAA_VZA_B%02d.img' % i for i in [2, 3, 4, 8, 11, 12]
]
sza = np.zeros((10980, 10980))
saa = sza.copy()
from grab_s2_toa import read_s2
tile = ''.join(example_file.split('/')[-8:-5])
s2 = read_s2('/store/S2_data/',
tile,
date.year,
date.month,
date.day,
bands=['B02', 'B03', 'B04', 'B08', 'B11', 'B12'])
s2.get_s2_angles()
sa_files = [s2.angles['saa'], s2.angles['sza']]
ret = MCD43_SurRef(mcd43_dir,
example_file,
date.year,
doy, [sa_files, va_files],
sun_view_ang_scale=[1., 0.01],
bands=bands,
reproject=False)