Inverting a microwave backscattering model by the use of a neural network for the estimation of soil moisture

The theoretical backscattering model from Fung et al. (1992) was used to simulate the reflection of an electromagnetic wave from a synthetic aperture radar (SAR) on a bare soil surface depending on the soil properties. These data were applied to a neural network (NN) as a training set to retrieve the soil moisture from the backscattering coefficient. Several network configurations were tested and three are discussed. In the next step the trained networks were fed with remotely sensed data from the SAR on board of ERS-2 (European Remote Sensing Satellite) and data from the airborne `Experimental SAR´ (ESAR) built by the Institute of Radiofrequencies of the `German Aerospace Center´ (DLR). The root mean square error (RMSE) between soil moisture calculated from the network and measured in situ during the overflights was below 5% volumetric soil moisture