A synergistic approach for soil moisture estimation using modified Dubois model with dual-polarized SAR and optical satellite data

Abstract

This paper discusses about an estimation of soil moisture in agricultural region using SAR data with the use of HH and HV polarization. In this study the semi empirical approach derived by Dubois et al ( 1 ) was modified to work using (σdeg;HH) and σ°VV) so that soil moisture can be obtained for the larger area extent. The optical remote sensing is helps to monitor changes in vegetation biomass and canopy cover surface reflectance by using NDVI and LAI from which the site suitability from different land use/land cover are identified. The second use involves retrieve the backscattering coefficient valuesσ°) derived from SAR for soil moisture studies. In SAR techniques, the relative surface roughness can be directly estimate using surface roughness derivation empirical algorithms. The mid incidence angle is used to overcome the incidence angle effect and it worked successfully to this study. The modified Dubois Model (MDM) in combination with The Topp’s et al ( 2 ) model is used to retrieve soil moisture. These two models have equations (HH, VV) and two independent variables i.e. root mean square height (s) and dielectric constant (epsilon). The linear regression analysis is performed and the surface roughness derived from SAR is well correlated with ground surface roughness having the value of (r 2 = 0.69). By using the dielectric constant (epsilon) the modified Dubois model in combination with Topp’s model are performed and the soil moisture is derived from SAR having value of (r 2 = 0.60). Thus, the derived model is having good scope for soil moisture monitoring with present availability of SAR datasets.