157 lines
5.9 KiB
Python
157 lines
5.9 KiB
Python
import numpy as np
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import xarray as xr
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from brdf_utils import ADF_OCP, solve_2nd_order_poly, drop_unused_coords
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''' Morel et al. (2002) BRDF 校正
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未包含 R gothic
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f/Q 查找表与 NASA SeaDAS 中的一致（详见 BRDF_M02SeaDAS.nc 属性）
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使用 OC4ME 叶绿素反演，进行一次迭代，未应用收敛标准
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'''
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""" M02 系数的类 """
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class Coeffs():
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def __init__(self, foq):
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self.foq = foq
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""" Class for M02 BRDF model """
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class M02SeaDAS:
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""" Initialise M02 model: BRDF LUT, coeffs, OC4ME parameters, water IOPs LUT
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Note: bands are fixed and defined at class initilization, but could be initialized in init_pixels if needed
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"""
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def __init__(self, bands, adf=None):
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if adf is None:
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adf = ADF_OCP
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# Check required bands are existing, within a 25 nm threshold
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self.bands = bands
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threshold = 25.
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bands_required = [442.5, 490, 510, 560]
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bands_ref = bands.sel(bands=bands_required, method='nearest')
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for band_ref, band_required in zip(bands_ref, bands_required):
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assert abs(band_ref - band_required) < threshold, 'Band %d nm missing or too far' % band_ref
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self.b442 = float(bands_ref[0].item())
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self.b490 = float(bands_ref[1].item())
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self.b510 = float(bands_ref[2].item())
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self.b560 = float(bands_ref[3].item())
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# Read BRDF LUT and compute default coeffs
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LUT_OCP = xr.open_dataset(adf % 'M02SeaDAS',engine='netcdf4')
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self.LUT = xr.Dataset()
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# Homogeneise naming convention with other methods... (PZA --> OZA transformation comes below...)
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self.LUT['foq'] = LUT_OCP.f_over_q_LUT.rename({'SZA_FOQ':'theta_s',
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'PZA_FOQ':'theta_v',
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'RAA_FOQ':'delta_phi',
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'log_chl_FOQ':'log_chl_foq'})
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# Index of refraction
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self.n_w = float(LUT_OCP.water_refraction_index.data)
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# Remove trivial aot indexation
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self.LUT['foq'] = self.LUT['foq'].squeeze()
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#
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self.coeffs0 = self.interp_geometries(0., 0., 0.)
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self.coeffs = Coeffs(np.nan)
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# Parameters for the OC4ME chl retrieval
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self.OC4MEcoeff = LUT_OCP.log10_coeff_LUT.values
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self.OC4MEepsilon = LUT_OCP.oc4me_epsilon
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self.OC4MEchl0 = float(LUT_OCP.oc4me_chl0.values)
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self.niter = LUT_OCP.oc4me_niter
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""" Initialize pixel: coefficient at current geometry and water IOP at current bands """
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def init_pixels(self, theta_s, theta_v, delta_phi):
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self.coeffs = self.interp_geometries(theta_s, theta_v, delta_phi)
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# Cache wavelength and chlorophyll bounds for fast access in forward()
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self.min_wavelength_FOQ = float(np.min(self.coeffs.foq.wavelengths_FOQ))
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self.max_wavelength_FOQ = float(np.max(self.coeffs.foq.wavelengths_FOQ))
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self.min_log_chl_foq = float(np.min(self.coeffs.foq.log_chl_foq) / np.log(10))
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self.max_log_chl_foq = float(np.max(self.coeffs.foq.log_chl_foq) / np.log(10))
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""" Interpolate coefficients """
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def interp_geometries(self, theta_s, theta_v, delta_phi):
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# Transform PZA to VZA (Snell's refraction Law)
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theta_v = np.rad2deg(np.arcsin(np.sin(np.deg2rad(theta_v)) / self.n_w))
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theta_v_0 = np.clip(theta_v,
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float(np.min(self.LUT.theta_v)),
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float(np.max(self.LUT.theta_v)))
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foq = self.LUT.foq.interp(theta_s=theta_s, theta_v=theta_v_0, delta_phi=delta_phi)
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return Coeffs(foq)
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""" Compute remote-sensing reflectance"""
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def forward(self, ds, normalized=False):
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if normalized:
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coeffs = self.coeffs0
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min_wl = float(np.min(coeffs.foq.wavelengths_FOQ))
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max_wl = float(np.max(coeffs.foq.wavelengths_FOQ))
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min_chl = float(np.min(coeffs.foq.log_chl_foq) / np.log(10))
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max_chl = float(np.max(coeffs.foq.log_chl_foq) / np.log(10))
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else:
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coeffs = self.coeffs
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min_wl = self.min_wavelength_FOQ
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max_wl = self.max_wavelength_FOQ
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min_chl = self.min_log_chl_foq
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max_chl = self.max_log_chl_foq
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wave_foq = np.clip(ds['bands'], min_wl, max_wl)
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log10_chl_foq = np.clip(ds['log10_chl'], min_chl, max_chl)
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# f/Q LUT indexed with ln(CHL), i.e. log_e(CHL)
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log_chl_foq = log10_chl_foq * np.log(10)
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# Merged dual interpolation into single operation for better performance
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forward_mod = coeffs.foq.interp(
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wavelengths_FOQ=wave_foq,
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log_chl_foq=log_chl_foq,
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method='linear'
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)
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return forward_mod
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""" Apply QAA to retrieve IOP (omega_b, eta_b) from Rrs """
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def backward(self, ds, iter_brdf):
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Rrs = ds['nrrs']
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# Local renaming of bands (pre-converted to float in __init__)
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b442 = self.b442
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b490 = self.b490
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b510 = self.b510
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b560 = self.b560
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# Apply upper and lower limits to Rrs(665) #TODO check if not finite or missing?
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Rrs442 = Rrs.sel(bands=b442)
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Rrs490 = Rrs.sel(bands=b490)
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Rrs510 = Rrs.sel(bands=b510)
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Rrs560 = Rrs.sel(bands=b560)
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# Drop unused coordinates to reduce overhead
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Rrs442 = drop_unused_coords(Rrs442)
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Rrs490 = drop_unused_coords(Rrs490)
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Rrs510 = drop_unused_coords(Rrs510)
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Rrs560 = drop_unused_coords(Rrs560)
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# Compute the OC4ME "R"
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ds['log10_chl_OC4ME_Ratio'] = np.log10(np.max([Rrs442, Rrs490, Rrs510], axis=0) / Rrs560)
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ds['log10_chl'] = 0 * ds['log10_chl_OC4ME_Ratio']
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# Apply OC4ME 5-degree polynomial using numpy for better performance
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poly = np.polynomial.polynomial.polyval(
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ds['log10_chl_OC4ME_Ratio'].values,
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self.OC4MEcoeff
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)
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ds['log10_chl'].values = poly
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return ds |