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s2_aerosolNew.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 ddv import ddv
from glob import glob
from scipy import signal, ndimage
import cPickle as pkl
from osgeo import osr
from scipy.ndimage import binary_dilation, binary_erosion
from smoothn import smoothn
from grab_s2_toa import read_s2
from multi_process import parmap
from reproject import reproject_data
from grab_brdf import MCD43_SurRef, array_to_raster
from grab_uncertainty import grab_uncertainty
from atmo_paras_optimization_new import solving_atmo_paras
from spatial_mapping import Find_corresponding_pixels, cloud_dilation
from emulation_engine import AtmosphericEmulationEngine
from psf_optimize import psf_optimize
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/ucfajlg/Data/python/S2S3Synergy/optical_emulators',
mcd43_dir = '/data/selene/ucfajlg/Ujia/MCD43/',
s2_toa_dir = '/home/ucfafyi/DATA/S2_MODIS/s_data/',
global_dem = '/home/ucfafyi/DATA/Multiply/eles/global_dem.vrt',
wv_emus_dir = '/home/ucfafyi/DATA/Multiply/emus/wv_msi_retrieval.pkl',
cams_dir = '/home/ucfafyi/DATA/Multiply/cams/',
s2_tile = '29SQB',
s2_psf = None,
qa_thresh = 255,
aero_res = 3050, # resolution for aerosol retrival in meters should be larger than 500
reconstruct_s2_angle = True):
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.s2_toa_dir = s2_toa_dir
self.global_dem = global_dem
self.wv_emus_dir = wv_emus_dir
self.cams_dir = cams_dir
self.s2_tile = s2_tile
self.s2_psf = s2_psf
self.s2_u_bands = 'B02', 'B03', 'B04', 'B08', 'B11', 'B12', 'B8A', 'B09' #bands used for the atmo-cor
self.band_indexs = [1, 2, 3, 7, 11, 12]
self.boa_bands = [469, 555, 645, 869, 1640, 2130]
self.full_res = (10980, 10980)
self.aero_res = aero_res
self.mcd43_tmp = '%s/MCD43A1.A%d%03d.%s.006.*.hdf'
self.reconstruct_s2_angle = reconstruct_s2_angle
self.s2_spectral_transform = [[ 1.06946607, 1.03048916, 1.04039226, 1.00163932, 1.00010918, 0.95607606, 0.99951677],
[ 0.0035921 , -0.00142761, -0.00383504, -0.00558762, -0.00570695, 0.00861192, 0.00188871]]
def _load_xa_xb_xc_emus(self,):
xap_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xap.pkl'%(self.s2_sensor))[0]
xbp_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xbp.pkl'%(self.s2_sensor))[0]
xcp_emu = glob(self.emus_dir + '/isotropic_%s_emulators_*_xcp.pkl'%(self.s2_sensor))[0]
f = lambda em: pkl.load(open(em, 'rb'))
self.emus = parmap(f, [xap_emu, xbp_emu, xcp_emu])
def repeat_extend(self,data, shape=(10980, 10980)):
da_shape = data.shape
re_x, re_y = int(1.*shape[0]/da_shape[0]), int(1.*shape[1]/da_shape[1])
new_data = np.zeros(shape)
new_data[:] = -9999
new_data[:re_x*da_shape[0], :re_y*da_shape[1]] = np.repeat(np.repeat(data, re_x,axis=0), re_y, axis=1)
return new_data
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 _get_ddv_aot(self, selected_img):
red_emus = self.emus[0][3], self.emus[1][3], self.emus[2][3]
blue_emus = self.emus[0][1], self.emus[1][1], self.emus[2][1]
sza = self.sza
blue_vza = self.vza[0]
red_vza = self.vza[2]
blue_raa = self.vaa[0] - self.saa
red_raa = self.vaa[2] - self.saa
b2, b4, = selected_img['B02']/10000., selected_img['B04']/10000.
b8, b12 = selected_img['B08']/10000., selected_img['B12']/10000.
b12 = np.repeat(np.repeat(b12, 2, axis = 1), 2, axis = 0)
solved = ddv(b2, b4, b8, b12, 'msi', sza,
np.array([blue_vza, red_vza]),
np.array([blue_raa, red_raa]),
self.elevation, self.tcwv, self.tco3, \
red_emus = red_emus, blue_emus = blue_emus)._ddv_prior()
return solved
def _get_tcwv(self, img, vza, vaa, sza, saa, ele):
b8a, b9 = np.repeat(np.repeat(img['B8A']*0.0001, 2, axis=0), 2, axis=1)[self.Hx, self.Hy],\
np.repeat(np.repeat(img['B09']*0.0001, 6, axis=0), 6, axis=1)[self.Hx, self.Hy]
wv_emus = pkl.load(open(self.wv_emus_dir, 'rb'))
vza, vaa = vza['B09'][self.Hx, self.Hy], vaa['B09'][self.Hx, self.Hy]
sza, saa = sza[self.Hx, self.Hy], saa[self.Hx, self.Hy]
elevation = ele[self.Hx, self.Hy]
inputs = np.array([b9, b8a, vza, sza, abs(saa-vaa), elevation]).T
tcwv_mask = b8a < 0.1
tcwv = np.zeros(self.full_res)
tcwv[:] = np.nan
tcwv_unc = tcwv.copy()
s2_tcwv, s2_tcwv_unc, _ = wv_emus.predict(inputs, do_unc = True)
if tcwv_mask.sum() >= 1:
s2_tcwv[tcwv_mask] = np.interp(np.flatnonzero( tcwv_mask), \
np.flatnonzero(~tcwv_mask), s2_tcwv[~tcwv_mask]) # simple interpolation
tcwv [self.Hx, self.Hy] = s2_tcwv
tcwv_unc[self.Hx, self.Hy] = s2_tcwv_unc
self.tcwv = np.nanmean(tcwv .reshape(self.num_blocks, self.block_size, \
self.num_blocks, self.block_size), axis = (3,1))
self.tcwv_unc = np.nanmax (tcwv_unc.reshape(self.num_blocks, self.block_size, \
self.num_blocks, self.block_size), axis = (3,1))
def _get_psf(self, selected_img):
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 = np.ma.array(self.s2_boa[4])
qa, cloud = self.s2_boa_qa[4], self.cloud
psf = psf_optimize(high_img, high_indexs, low_img, qa, cloud, 0.16)
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()))
return xstd, ystd, ang, xs, ys
def _mcd43_cloud(self,flist, lx, ly, example_file, boa, b12):
g = gdal.BuildVRT('', list(flist))
temp_data = np.zeros((g.RasterYSize, g.RasterXSize))
temp_data[lx, ly] = boa[5,:]
self.boa_b12 = reproject_data(array_to_raster(temp_data, g), example_file, outputType = gdal.GDT_Float32).data
toa_b12 = np.repeat(np.repeat(b12/10000., 2, axis=0), 2, axis=1)
mask = abs(self.boa_b12 - toa_b12)>0.1
emask = binary_erosion(mask, structure=np.ones((3,3)).astype(bool), iterations=15)
dmask = binary_dilation(emask, structure=np.ones((3,3)).astype(bool), iterations=100)
self.cloud = self.s2.cloud | dmask
#import pylab as plt
#plt.imshow(self.cloud)
#plt.show()
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.')
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))
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 + (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.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.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 >= 0.001, 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...')
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 _read_cams(self, example_file, parameters = ['aod550', 'tcwv', 'gtco3']):
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 para in 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))
if mask.sum()>=1:
data[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), data[~mask])
results.append(data)
return results
def solving_s2_aerosol(self,):
self.s2_logger = logging.getLogger('Sentinel 2 Atmospheric Correction')
self.s2_logger.setLevel(logging.INFO)
if not self.s2_logger.handlers:
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
self.s2_logger.addHandler(ch)
self.s2_logger.propagate = False
self.s2_sensor = 'msi'
self.s2_logger.info('Doing Sentinel 2 tile: %s on %d-%02d-%02d.'%(self.s2_tile, self.year, self.month, self.day))
self.block_size = int(self.aero_res/10)
self.num_blocks = int(np.ceil(self.full_res[0]/self.block_size))
self.solved = self._s2_aerosol()[0].reshape(3, self.num_blocks, self.num_blocks)
self.s2_logger.info('Finished retrieval and saving them into local files.')
g = gdal.Open(self.s2_file_dir+'/B04.jp2')
xmin, ymax = g.GetGeoTransform()[0], g.GetGeoTransform()[3]
projection = g.GetProjection()
para_names = 'aot', 'tcwv', 'tco3'
for i,para_map in enumerate(self.solved):
xres, yres = self.block_size*10, self.block_size*10
geotransform = (xmin, xres, 0, ymax, 0, -yres)
nx, ny = self.num_blocks, self.num_blocks
dst_ds = gdal.GetDriverByName('GTiff').Create(self.s2_file_dir + \
'/%s.tif'%para_names[i], ny, nx, 1, gdal.GDT_Float32)
dst_ds.SetGeoTransform(geotransform)
dst_ds.SetProjection(projection)
dst_ds.GetRasterBand(1).WriteArray(para_map)
dst_ds.FlushCache()
dst_ds = None
self.aot_map, self.tcwv_map, self.tco3_map = self.solved
if __name__ == "__main__":
aero = solve_aerosol( 2016, 1, 24, mcd43_dir = '/data/selene/ucfajlg/Hebei/MCD43/', s2_toa_dir = '/store/S2_data/',\
emus_dir = '/home/ucfafyi/DATA/Multiply/emus/', s2_tile='50SMG', s2_psf=None)
aero.solving_s2_aerosol()
#solved = aero.prepare_modis()