EFFECT OF SUB-PIXEL VARIABILITY AND LAND-COVER ON SOIL MOISTURE RETRIEVAL FROM RADARSAT-1 DATA

Abstract

The dynamic of soil moisture is generally affected by the spatial variation in soil surface characteristics such as land cover, vegetation density, soil texture, and soil material. The main purpose of this project is to develop neural network algorithm for soil moisture retrieval from active microwave data. A back-propagation neural network has been used to estimate the soil moisture from Synthetic Aperture Radar data. Soil moisture data with a spatial resolution of 800 m acquired during the SGP97 campaign, were used as truth data in the training and the validation processes. In addition to backscatter values retrieved from RADARSAT-1 image, normalized difference vegetation index (NDVI), land cover and soil texture have been added as an input to neural network algorithm. The effects of sub-pixels variability of the NDVI and land cover type on the retrieval of soil moisture have been investigated by comparing the measured and the predicted soil moisture. Further, all training and validation pixels (800 m resolution) have been labeled as either homogeneous or heterogeneous based on the occurrence of the same land cover type. 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, where the surface wetness dominated the vegetation contribution to the radar backscatter