Abstract
The characterization of snow conditions and the estimate 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. A sensitivity analysis was carried out by using datasets of insitu snow measurements (snow depth, density, snow grain radius, temperature and wetness) collected in South Tyrol region, north-eastern Italy. Simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model were considered for interpreting and assessing the experimental findings. Two retrieval algorithms for SWE estimation from the C- and X-band SAR frequencies were implemented. The retrieval 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, successively, is applied to a selection of CSK StripMap HIMAGE scenes collected over the test area. The obtained results are promising, and pave the way for further analysis and validation to exploit the potential of SAR in snow parameter retrieval.