Soil-Moisture Estimation From X-Band Data Using Tikhonov Regularization and Neural Net

This paper introduces soil-moisture parameter retrieval using high-resolution vertically polarized (VV) Spotlight TerraSAR-X data. The soil-moisture estimation of bare and vegetated areas is considered by using volumetric scattering, which is modeled with a bare-soil component and a component reflecting vegetation. The unknown coefficients of the soil-moisture model are estimated using the Tikhonov regularization scheme. A neural network is used in order to distinguish volumetric scattering from all the other types of scattering. The estimated volumetric-soil-moisture parameters are further enhanced by using a supervised feedforward backpropagation neural network. The proposed algorithm based on the Tikhonov regularization scheme, in combination with neural networks, provides good results for estimating volumetric-soil-moisture in an area covered with a small vegetation canopy.