Abstract
Synthetic aperture radar (SAR) has potential use as a means of measuring soil-water content from space or high altitude airborne platforms. However, the signal from other landscape features, such as topography, vegetation, and surface roughness, may overwhelm and confound the soil moisture signal. Previous researchers tried to correct for topographic effects using incident angle (θ) as the most significant landscape variable. Those corrections were not effective because of the poor correlation between the SAR backscatter coefficient (σ 0 ) and θ. This may be attributed to surface roughness, vegetation effects, or low resolution digital elevation data. In this article, we determined the effects of vegetation, surface roughness, and topography on σ 0 . By comparing the normalized difference vegetation index (NDVI) and the SAR-based vegetation index (SBVI), it was found that the sparse vegetation cover of sagebrush in the rangeland areas of the Reynolds Creek Experimental Watershed (RCEW) does not affect L band HH polarization SAR σ 0 . Root mean square (RMS) height of surface (h), determined using L band copolarized SAR σ 0 in an empirical algorithm (Dubois et al., 1995), helped in stratifying the study area into low and high surface roughness areas. The high surface roughness areas, determined using the algorithm, were found to match with very rocky and extremely rocky areas, classified using detailed soil type information in a Geographic Information System (GIS). The performance of a θ-based correction function improved significantly after incorporating h into the model.
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