Neural Networks Applications for the Remote Sensing of Hydrological parameters

The main ANN based retrieval algorithms developed at the Institute of Applied Physics (IFAC) are reviewed here. These algorithms aim at retrieving the main hydrological parameters, namely the soil moisture content (SMC), the plant water content (PWC) of agricultural vegetation, the woody volume of forests (WV) and the snow depth (SD) or snow water equivalent (SWE), from data collected by active (SAR/scatterometers) and passive (radiometers) microwave sensors operating from space. Taking advantage of the fast computation, ANN are able to generate output maps of the target parameter at both local and global scales, with a resolution varying from hundreds of meters to tens of kilometres, depending on the considered sensor. A peculiar strategy adopted for the training, which has been obtained by combining satellite measurements with data simulated by electromagnetic models (based on the radiative transfer theory, RTT), made these algorithms robust and site-independent. The obtained results demonstrated that ANN are a powerful tool for estimating the hydrological parameters at different spatial scales, provided they have been trained with consistent datasets, made up by both experimental and theoretical data.