Résumé
The aim of this study was to develop an inversion approach to estimate surface soil moisture from X band SAR data over irrigated grassland areas. This approach is based on the coupling between Synthetic Aperture Radar and optical images through the Water Cloud Model. An inversion technique based on multi-layer perceptron neural networks was used to invert the WCM for soil moisture estimation. Three inversion configurations were defined: (1) HH polarization, (2) HV polarization, and (3) both HH and HV polarizations, all including the Leaf Area Index derived from optical images. For the three inversion configurations, the NNs were trained and validated using a noisy synthetic dataset generated by the WCM for a wide range of soil moisture and LAI values. The trained NNs were then validated from a real dataset. The use of X band SAR measurements in HH polarization yields more precise results on soil moisture estimates.