Spatial Mapping of Soil Moisture Using Radarsat-1 Data

In this research, a back-propagation neural network was used to retrieve and map the surface soil moisture in Oklahoma (97d35'W, 36d15'N) from Synthetic Aperture Radar data acquired by RADARSAT-1 satellite. In addition to SAR backscattering, different vegetation-related information (vegetation optical depth and Normalized Difference Vegetation Index) have been added as additional inputs to the neural network algorithm. The soil moisture data measured by Electronically Scanned Thinned Array Radiometer during the SGP97 campaign were used as truth data in the training and the validation processes. All the training and validation pixels have been labeled as either homogeneous or heterogeneous based on land cover type and number of sub-pixels of 25m resolution. The results showed that homogeneous pixels are more likely to have better accuracy than heterogeneous pixels in soil moisture classification. A better correlation between soil moisture and SAR backscattering was found in areas with high soil moisture content. The modeling results have shown that the retrieval of soil moisture in highly vegetated areas was less accurate than bare soil areas. Further, the same results have shown that the additions of vegetation optical depth and Normalized Difference Vegetation Index as additional input to the SAR data had a significant effect on the overall classification accuracy.