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Journal of Applied Remote Sensing

Multilayer soil model for improvement of soil moisture estimation using the small perturbation method
Author(s): Kaijun Song; Xiaobing Zhou; Yong Fan
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Paper Abstract

A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are 5.2%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields.

Paper Details

Date Published: 1 December 2009
PDF: 14 pages
J. Appl. Remote Sens. 3(1) 033567 doi: 10.1117/1.3277666
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Kaijun Song, Montana Tech (United States)
Xiaobing Zhou, Montana Tech (United States)
Yong Fan, Univ. of Electronic Science and Technology of China (China)

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