Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors

Among the algorithms used for the retrieval of SMC from microwave sensors (both active, such as Synthetic Aperture Radar-SAR, and passive, radiometers), the artificial neural networks (ANN) represent the best compromise between accuracy and computation speed. ANN based algorithms have been developed at IFAC, and adapted to several radar and radiometric satellite-sensors, in order to generate SMC products at a resolution varying from hundreds of meters to tens of kilometers according to the spatial scale of each sensor. These algorithms, which are based on the ANN techniques for inverting theoretical and semi-empirical models, have been adapted to the C- to Ka- band acquisitions from spaceborne radiometers (AMSR-E/AMSR-2), SAR (Envisat/ASAR, Cosmo-SkyMed) and real aperture radar (MetOP ASCAT). Large datasets of co-located satellite acquisitions and direct SMC measurements on several test sites worldwide have been used along with simulations derived from forward electromagnetic models for setting up, training and validating these algorithms. An overall quality assessment of the obtained results in terms of accuracy and computational cost was carried out, and the main advantages and limitations for an operational use of these algorithms were evaluated. This technique allowed the retrieval of SMC from both active and passive satellite systems, with accuracy values of about 0.05 m(3)/m(3) of SMC or better, thus making these applications compliant with the usual accuracy-requirements for SMC products from space. (C) 2015 Elsevier B.V. All rights reserved.