Retrieving surface roughness and soil moisture from SAR data using neural networks

An inversion technique based on neural networks has been implemented to estimate surface roughness and soil moisture over bare fields using ERS and RADARSAT data. The neural networks were trained with a simulated data set generated from the Integral Equation Model. Later the networks were applied to an experimental data set spanning a wide range of surface roughness and soil moisture, with backscattering coefficients for three radar configurations (VV-23degrees, HH-39degrees, HH-47degrees). Approaches based on two and three radar image configurations were examined and tested. Although the three-image configuration produces slightly more accurate results, a two-image configuration gives results of comparable accuracy when a favourable combination of incidence angles is adopted. Soil moisture and surface roughness were estimated respectively at about 7.6% and 0.47 cm using the root mean square error.