A comparison of two models to predict soil moisture from remote sensing data of RADARSAT II

This study investigates the performance of empirical and semiempirical models to predict soil moisture from the data of RADARSAT II synthetic aperture radar (SAR) for the Yarmouk basin in Jordan. Data of SAR were obtained forMay and June 2010 and were processed to obtain backscatter (sigma(o)) data for the study area. Results showed significant correlations between soil moisture content (m(v)) and horizontally polarized sigma(o), with coefficient of determination (R-2) of 0.64. The root mean square error for the SAR volumetric soil moisture content was 0.09 and 0.06 m(3)/m(3) for the empirical and semiempirical regression models, respectively. Both models had different clustering patterns in the soil moisture maps in the study area. The spatial agreement between maps of soil moisture was in the range of 55 to 65 % when the maps were reclassified based on intervals of 5 % mv for both models. In terms of soil moisture interval, both models showed that most of soil moisture changes between the two images (dates) were in the range of +/- 5 %. Some high differences in.mv were observed between the two models. These were mainly attributed to the non-inverted pixels in the soil moisture maps produced by the semiempirical model. Therefore, this model may be applied for a limited range of soil moisture prediction. The use of regression model could predict a wider range for soil moisture when compared with the semiempirical model. However, more work might be needed to improve the empirical model before scaling it up to the whole study area.