Abstract
This research intends to exploit the potentialities of multimission SAR data at X-, C- and L-band to monitor the snowpack and alpine soil. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism were monitored and different algorithms are proposed for the retrieval of these parameters. Several methods and techniques have been used depending on the parameter to be returned: machine learning approaches as Artificial Neural Networks and Random Forest were implemented in case of snow parameter retrieval; interferometric techniques are also involved in case of snow and soil displacement as rock glaciers. Microwave models are used to better explain the snow and soil behavior in the microwave range (AIEM, Oh, SFT and DMRT-QCA), and SNOWPACK physical model for corroborate experimental data. Moreover, experimental activities have been conducted in two selected sites in Northern Italy, which are characterized, in some cases, by the presence of permafrost and they are covered by alpine snow during the winter and spring period. Preliminary and consistent results have been obtained in terms of snow parameters and soil displacement. This comprehensive approach enhances the ability to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.