Abstract
A generalized and flexible snow Bidirectional Reflectance Distribution Function (BRDF) model is proposed to describe the snow anisotropic properties in the spectral ranges from UltraViolet (UV) to Short-Wave InfraRed (SWIR). The new development belongs to the Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance (FASMAR) family. FASMAR is developed, applied to and validated by ground-, aircraft- and satellite observations. FASMAR is a kernel-driven BRDF model. It contains four major kernels: isotropy kernel, asymptotic radiative transfer kernel, single-scattering kernel, and auxiliary kernels. These kernels enable FASMAR to characterize the angular-dependent scattering features and wavelength-dependent absorbing patterns of the snow BRDF properties. The comparison between FASMAR and RossThick-LiSparseReciprocal-Snow (RTLSRS) model were performed using radiative transfer simulations, ground-, aircraft- and satellite observations at selected wavelengths 380, 480, 670, 870, 1220, and 2200 nm. For Solar Zenith Angle (SZA) smaller than 70°, FASMAR shows somehow better accuracy compared to the RTLSRS model. FASMAR’s accuracy keeps quite stable for large SZAs (SZA larger than 70°). The relative difference between FASMAR calculations and radiative transfer simulations is within 5% for wavelengths between 380 and 2200 nm. A similar accuracy of FASMAR is observed compared to aircraft BRDF measurements, with a relative difference of less than 5%. FASMAR also shows good agreement with ground-based and satellite observations, with correlation coefficients larger than 0.9 and relative errors smaller than 2% for UV–vis and 4% for NIR-SWIR spectral ranges. The comparisons additionally reveal that the accuracy of FASMAR is not strongly affected by the a priori knowledge of the snow grain size. FASMAR can provide snow BRDF estimation with good accuracy in spectral regions from UV to SWIR under all observation/illumination geometries. FASMAR is used support the operational retrieval of the snow properties from satellite observations.
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More From: ISPRS Journal of Photogrammetry and Remote Sensing
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