Machine learning applications for classification and retrieval of surface parameters from gnss-r.

This study focuses on the retrieval of soil moisture (SMC) and forest Aboveground Biomass (AGB), and on the classification of fire disturbances in forests by using the NASA's Cyclone GNSS ( CyGNSS) data over land. Retrieval and classification algorithms, based on machine learning ( ML) techniques, as Supported vector machines (SVM), Artificial Neural Networks (ANN) and Random Forests are implemented and validated against reference data from in-situ measurements and EO products. The research, which was carried out in the framework of two ESA project, has the twofold aim of further assessing the potential of GNSS-R for land applications and of defining retrieval concepts to be applied to the ESA's SCOUT 2 HydroGNSS satellite mission.