Multi-Frequency SAR Images for SWE Retrieval in Alpine Areas Through Machine Learning APPROACHES

The characterization of snow conditions and the estimation of snow water equivalent (SWE) are the main goals of this paper, achieved through the exploitation of multi-frequency SAR data at both C- and X-bands from Sentinel-1 (S-1) and COSMO-SkyMed (CSK) satellites, respectively. Dry/wet snow conditions have first been assessed using C-band S-1 images. Subsequently, a sensitivity analysis was carried out by using datasets of in-situ snow measurements (i.e. snow depth, density, snow grain radius, temperature and wetness) collected in South Tyrol region, in north-eastern Italy. Simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model were considered to interpret and assess the experimental findings. Two retrieval algorithms for SWE estimation from X-band SAR data were implemented. These algorithms are based on machine learning approaches, i.e. Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The training of the algorithms accounts for experimental data and DMRT model simulations and, then is applied to a selection of X-band CSK StripMap HIMAGE scenes collected over the test area. The results are promising, and pave the way for further analysis and validation to exploit the potential of SAR for snow parameter retrieval.