Get reliable rangeland science

There have been several efforts to utilize satellite-based synthetic aperture radar (SAR) measurements to determine surface soil moisture (to 5 cm) conditions of rangeland regions. The results have been mixed since the relation between the SAR signal and surface soil moisture is confounded by variations in topographic features, surface roughness and vegetation density. We designed an experiment to investigate the sensitivity of C-band SAR backscatter ([sigma]0) to surface soil moisture ([theta]s) in a semiarid rangeland and to test a data-fusion approach based on both optical (Landsat TM) and radar (ERS-2 SAR) measurements to improve regional estimates of surface soil moisture content. The data-fusion approach [Sano, E.E. 1997. Sensitivity analysis of C- and Ku-band synthetic aperture radar data to soil moisture content in a semiarid regions. Ph.D. Dissertation. University of Arizona, AZ] utilized the difference between dry- and wet-season SAR [sigma]0 to normalize roughness effects, and utilized surface reflectance in optical wavelengths to account for differences in vegetation density. We focused the study on three flat, uniformly vegetated sites of known surface roughness, monitored variations in surface soil moisture, vegetation density and SAR signal over time, and obtained eight optical/SAR image pairs throughout the dry and wet seasons. For these sparsely vegetated sites during this dry year (1997), we found that the SAR signal was not significantly attenuated by sparse green vegetation cover (greenleafareaindex<0.35) and dense standing brown vegetation cover (brown leaf area index up to 1.5). Consequently, the optical data was not required for this application, and the approach could be implemented by simply taking the difference between the dry- and wet-season SAR [sigma]0 ([sigma]0-[sigma]dry0). For a data set of eight dates at three study sites, we confirmed that the relation between ERS-2 C-band SAR [sigma]0 and [theta]s was weak (r2=0.27); yet for the same data set, that the relation between [sigma]0-[sigma]dry0 and [theta]s was strong and significant (r2=0.93). This study also raised two concerns: (1) the overall sensitivity of SAR [sigma]0 to [theta]s was relatively low, and (2) the approach required a high level of accuracy in the estimate of green leaf area level that may not be obtainable with standard optical remote sensing algorithms. In any case, the positive results from this study should encourage the use of a multi-temporal SAR and optical/SAR fusion for monitoring semiarid range conditions, and improving management of scarce resources.