A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data
Author(s) -
Roujean JeanLouis,
Leroy Marc,
Deschamps PierreYves
Publication year - 1992
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 - 10.1029/92jd01411
Subject(s) - remote sensing , opacity , scattering , surface (topology) , reflection (computer programming) , bidirectional reflectance distribution function , volume (thermodynamics) , series (stratigraphy) , optics , reflectivity , satellite , infrared , materials science , geology , physics , computer science , geometry , mathematics , astronomy , paleontology , quantum mechanics , programming language
A surface bidirectional reflectance model has been developed for the correction of surface bidirectional effects in time series of satellite observations, where both sun and viewing angles are varying. The model follows a semiempirical approach and is designed to be applicable to heterogeneous surfaces. It contains only three adjustable parameters describing the surface and can potentially be included in an algorithm of processing and correction of a time series of remote sensing data. The model considers that the observed surface bidirectional reflectance is the sum of two main processes operating at a local scale: (1) a diffuse reflection component taking into account the geometrical structure of opaque reflectors on the surface, and shadowing effects, and (2) a volume scattering contribution by a collection of dispersed facets which simulates the volume scattering properties of canopies and bare soils. Detailed comparisons between the model and in situ observations show satisfactory agreement for most investigated surface types in the visible and near‐infrared spectral bands. The model appears therefore as a good candidate to reduce substantially the undesirable fluctuations related to surface bidirectional effects in remotely sensed multitemporal data sets.
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