Evaluation of the Dubois, Oh, and IEM radar backscatter models over agricultural fields using C-band RADARSAT-2 SAR image data.
Merzouki, A., McNairn, H., and Pacheco, A. (2010). "Evaluation of the Dubois, Oh, and IEM radar backscatter models over agricultural fields using C-band RADARSAT-2 SAR image data.", Canadian Journal of Remote Sensing, 36(Suppl. 2), pp. S274-S286. doi : 10.5589/m10-055 Access to full text
Backscatter from synthetic aperture radar (SAR) is correlated with surface characteristics such as soil dielectric properties, surface roughness, and vegetation cover. The purpose of this study is to evaluate the capability of surface radar backscatter models to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band SAR responses. For validation purposes, ground measurements over 44 and 42 sampling sites in eastern Ontario and southern Manitoba, respectively, were carried out in the spring of 2008 simultaneous with satellite data acquisitions. A comparison was made between the backscatter coefficient results derived from three scattering models (the semi-empirical models of Dubois and Oh and the theoretical integral equation model (IEM)) and the SAR image backscatter. Discrepancies between measured radar backscatter coefficients and those predicted by the models have been investigated. Overall, the results show that semi-empirical approaches tend to overestimate the radar response, but correction factors of about 3.5 and 2.0 dB, respectively, were found sufficient to correct the Dubois HH and VV backscatter coefficients. The Oh model backscatter estimations were less variable than those derived from the Dubois model; however, a correction factor of about 5.0 dB was necessary in this case. Data simulated by the IEM showed significant fluctuations. This result was somewhat expected, since previous studies have shown that correlation length measurements are very sensitive to profile length, and a relatively short profile length was used in this study. Correlation agreements were significantly improved when using the IEM semi-empirical calibration technique. These results indicate that further work is needed to assess the requirement for correction factors, such that these models can be operationally implemented to estimate soil moisture in support of agricultural monitoring.
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