CRITICAL ANALYSIS OF SOIL MOISTURE RETRIEVAL ALGORITHM FOR RADARSAT-2 AND RISAT-1 DATA

Abstract

The scattering of microwave radiation from soil surface are greatly affected by the local terrain as well as by different soil parameters. The dielectric property of soil being related to the soil moisture provides a good mean to find out the soil moisture, because of which various researchers have developed many algorithms to exploit this feature of soil. l-lowever the SAR data available are not purely reflected from soil surface, but also vegetation and other land cover classes. It's a challenge to discover new technique which normalizes the effect of vegetation and finds out the backscattering value only dependent on the soil surface parameter. Different SAR images having different frequency of operation and polarization, interacts differently with different properties of land from which we can use the best possible parameters that related to soil moisture. As the study area also contains water and urban regions, so first the land cover is classified into various classes. By classification of SAR image, it divides the pixels into groups which belongs to the same land cover class and exhibits similar surface properties. In this dissertation classification is performed on SAR data and further masking has been applied to estimate the soil moisture only in vegetated and bare areas. Masked image has been fused with the optical (MODIS) data, which consists of spectral information of the Red and Infra-Red channels of the electromagnetic spectrum and the spectral reflectance of surface is different for different land cover classes. To enhance the vegetation and soil information, vegetation index is calculated which is called as Normalized difference vegetation index (NDVI). After classification, the inversion approach is applied to the radar signal for developing the relationship between NI)Vl and SAR image. By using the developed relation the soil moisture values are retrieved. The result of the approach is then checked out to find the limits of error (validation) on surface region and after validation of developed relation the soil moisture map from the SAR images are generated.