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l8_aerosol_old.py
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l8_aerosol_old.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 multi_process import parmap
from reproject import reproject_data
from get_brdf import get_brdf_six
from grab_uncertainty import grab_uncertainty
from atmo_paras_optimization_new import solving_atmo_paras
from psf_optimize import psf_optimize
from spatial_mapping import Find_corresponding_pixels
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/',
l8_tile = (123, 34),
l8_psf = None,
qa_thresh = 255,
aero_res = 3000, # 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.l8_tile = l8_tile
self.l8_psf = l8_psf
self.bands = [2, 3, 4, 5, 6, 7]
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 _extend_vals(self, val):
self.block_size = int(self.aero_res / 30.)
self.num_blocks = int(np.ceil(max(self.full_res) / self.block_size))
self.efull_res = self.block_size * self.num_blocks
temp = np.zeros((self.efull_res, self.efull_res))
temp[:] = np.nan
temp[:self.full_res[0], :self.full_res[1]] = val
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)
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
def _get_ddv_aot(self, toa, l8, tcwv, tco3, ele_data):
ndvi_mask = (((toa[5] - toa[2])/(toa[5] + toa[2])) > 0.5) & (toa[5] > 0.01) & (toa[5] < 0.25)
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() > 25000000:
random_choice = np.random.choice(len(Hx), 25000000, 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(l8.vza[0, Hx, Hy]))
blue_sza = np.cos(np.deg2rad(l8.sza[0, Hx, Hy]))
red_vza = np.cos(np.deg2rad(l8.vza[2, Hx, Hy]))
red_sza = np.cos(np.deg2rad(l8.sza[2, Hx, Hy]))
blue_raa = np.cos(np.deg2rad(l8.vaa[0, Hx, Hy] - l8.saa[0, Hx, Hy]))
red_raa = np.cos(np.deg2rad(l8.vaa[2, Hx, Hy] - l8.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 of cams prediction.'% p[min_ind])
self.aot += (p[min_ind] - self.aot.mean())
#self.costs = costs
#self.p = p
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 = (blue_sur - 0.25 * swif)**2
red_dif = (red_sur - 0.5 * swif)**2
cost = 0.5 * (blue_dif + red_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).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*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))
self._l8_aerosol()
if __name__ == '__main__':
aero = solve_aerosol(2017, 7, 10, l8_tile = (123, 34), mcd43_dir = '/home/ucfafyi/DATA/S2_MODIS/m_data/')
aero.solving_l8_aerosol()