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Evaluation of snow proprieties in the Alpine region using long ASAR time-series (ATS4S)

Project researchers

The capability of monitoring the evolution of the snow in mountainous areas is a key task for both understanding phenomena that are affecting the Planet Earth, such as the climate changes, and for supporting many application domains such as the water resource management and the risk prevention. 

The main objective of this project is the evaluation of the long-term behavior of some relevant snow proprieties in the mountainous regions using ENVISAT-ASAR data acquired in C-band. In order to properly analyze the temporal evolution of snow proprieties it is of paramount importance to involve in the analysis the highest possible number of multitemporal data available over the investigated test site. The study is intended to analyze a period of time spanning 10 years (from 2002 to 2012: the entire life of ENVISAT). The are of interest, is located in the challenging Alpine area covering the regions of Trentino-Alto-Adige (Italy) and Tirol (Austria). 

Based on these analyses, advanced algorithms for both data correction and parameter estimation will be developed. The data is preprocessed in order to obtain data that are absolute calibrated, co-registrated and with a reduced speckle. After data preprocessing, the snow parameters can be extracted by inverting the backscattering coefficient measured by the SAR system. The approach for the estimation will be based on advanced state-of-art machine learning methodologies. 

This project aims at exploiting the large database of ENVISAT-ASAR data in order to improve the understanding of the behavior of snow parameters in the Alpine region of Trentino-Alto-Adige (Italy) and Tirol (Austria). At the same time it aims at understanding how to make operative the exploitation of long-time SAR series for snow parameters retrieval in a challenging mountainous environment.
Wednesday, 16 October, 2013 to Thursday, 15 October, 2015
24 Months

PI: Prof. Lorenzo Bruzzone, Remote Sensing Laboratory (RSLab), Universita' degli Studi di Trento
Co-I: Dr. Francesca Bovolo, Remote Sensing for Digital Earth (RSDE), Fondazione Bruno Kessler


European Space Agency