Paper
17 December 1999 Derivation and validation of a new kernel for kernel-driven BRDF models
Xiaowen Li, Feng Gao, Liangzan Chen, Alan H. Strahler
Author Affiliations +
Abstract
Kernel-driven bidirectional reflectance (BRDF) models have recently been widely used for mapping albedo with multiangle remote sensing data such as ASAS or temporal AVHRR sequences. An Ambrals algorithm will be used to produce global BRDF and albedo products in the coming EOS era using multiangle reflectance data from the MODIS and MISR. Its operational version currently uses a combination of Ross-Thick and Li- Sparse-Reciprocal kernels which has been validated favorably over other kernels or combinations. To further improve the ability of extrapolation of the Ambrals kernel combination with better physical sense while keeping its data-fitting ability, a new kernel, Li-Transit, is derived and suggested to replace Li-Sparse-Reciprocal kernel in next version of Ambrals. We tested the new kernel combination against the current one and a few alternatives using 29 field collected BRDF data sets. The results show similar data fitting ability and more reliable extrapolation for albedo mapping. A test is also done by using the new combination and the current one to produce temporal albedo change maps of New England of U.S.A. using AVHRR images. Presented also is our recent study on scaling effect of Helmholtz principle of reciprocity, and discussion on application of a priori knowledge in kernel- driven BRDF model inversion.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaowen Li, Feng Gao, Liangzan Chen, and Alan H. Strahler "Derivation and validation of a new kernel for kernel-driven BRDF models", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); https://doi.org/10.1117/12.373123
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Cited by 15 scholarly publications.
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KEYWORDS
Bidirectional reflectance transmission function

Reflectivity

Data modeling

Remote sensing

Near infrared

Lithium

MODIS

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