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Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm
Author(s) -
Wanner W.,
Strahler A. H.,
Hu B.,
Lewis P.,
Muller J.P.,
Li X.,
Schaaf C. L. Barker,
Barnsley M. J.
Publication year - 1997
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/96jd03295
Subject(s) - bidirectional reflectance distribution function , remote sensing , moderate resolution imaging spectroradiometer , albedo (alchemy) , spectroradiometer , environmental science , satellite , land cover , pixel , algorithm , meteorology , computer science , geology , reflectivity , optics , physics , artificial intelligence , art , performance art , art history , land use , civil engineering , engineering , astronomy
This paper describes the theory and the algorithm to be used in producing a global bidirectional reflectance distribution function (BRDF) and albedo product from data to be acquired by the moderate resolution imaging spectroradiometer (MODIS) and the multiangle imaging spectroradiometer (MISR), both to be launched in 1998 on the AM‐1 satellite platform as part of NASA's Earth Observing System (EOS). The product will be derived using the kernel‐driven semiempirical Ambrals BRDF model, utilizing five variants of kernel functions characterizing isotropic, volume and surface scattering. The BRDF and the albedo of each pixel of the land surface will be modeled at a spatial resolution of 1 km and once every 16 days in seven spectral bands spanning the visible and the near infrared. The BRDF parameters retrieved and recorded in the MODIS BRDF/albedo product will be intrinsic surface properties decoupled from the prevailing atmospheric state and hence suited for a wide range of applications requiring characterization of the directional anisotropy of Earth surface reflectance. A set of quality control flags accompanies the product. An initial validation of the Ambrals model is demonstrated using a variety of field‐measured data sets for several different land cover types.

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