期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 47, 期 8, 页码 2519-2527出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2009.2014944
关键词
Moisture; polarimetric radar; soil; synthetic aperture radar (SAR); time series
Electromagnetic scattering from a rough surface is a function of both surface roughness and dielectric constant of the scattering surface. Therefore, in order to estimate soil moisture of a bare surface accurately from radar measurements, the effects of surface roughness must be compensated for properly. Several algorithms have been developed to estimate soil moisture from a polarimetric radar image, all with limited ranges of applicability. No theoretical algorithm has been reported to retrieve volumetric soil moisture of a vegetated surface. In this paper, we examine a different approach to estimate soil moisture that exploits the fact that the backscattering cross section from a natural object changes over short timescales mainly due to variations in soil moisture. We develop a model function that expresses copolarized backscattering cross sections (sigma(hh) and sigma(upsilon upsilon)) in terms of volumetric soil moisture using L-band experimental data for both bare and vegetated surfaces. In order to estimate soil moisture, two unknowns in the model function must be determined. We propose a viable approach to determine these two unknowns using combined radiometer and radar data. This time-series approach also provides a framework to utilize a priori knowledge on soil moisture to improve the retrieval accuracy of volumetric soil moisture. We demonstrate that this time-series algorithm is a simple and robust way to estimate soil moisture for both bare and vegetated surfaces.
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