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dc.contributor.authorPettinato S
dc.contributor.authorPaloscia S
dc.contributor.authorSanti E
dc.contributor.authorNotarnicola C
dc.contributor.authorCallegari M
dc.contributor.authorMarin C
dc.contributor.authorPalchetti E
dc.date.accessioned2020-04-01T16:41:06Z
dc.date.available2020-04-01T16:41:06Z
dc.date.issued2019
dc.identifier.urihttp://dx.doi.org/10.1109/IGARSS.2019.8898945
dc.identifier.urihttps://bia.unibz.it/handle/10863/13512
dc.description.abstractThe characterization of snow conditions and the estimate of snowpack parameters have been investigated in this paper, by taking into account both C- and X-band SAR data collected from Sentinel-1 (S-1) and Cosmo-SkyMed (CSK) satellites, respectively. Although C- and X-band are not the most suitable frequency for the retrieval of snow parameters due to the high penetration power inside snowpack, some results concerning the wet/dry snow status and both Snow Depth (SD) and Snow Water Equivalent (SWE) estimate can be achieved by using appropriate algorithms and models.A sensitivity analysis was carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test site in South Tyrol (Ulten Valley and Val Senales). This analysis provided indications on the sensitivity of C- and X-band backscattering to the target snow parameters. As a second step of the analysis, simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model have been considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from these frequencies is carried out. The algorithm is based on Artificial Neural Networks (ANN). The training of the algorithm accounts for experimental data and DMRT model simulations and, successively, it is applied to time series of CSK images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR in snow parameter retrieval.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relationIGARSS ; Yokohama : 28.7.2019 - 2.8.2019
dc.rights
dc.titleEXPLOITING THE SYNERGY BETWEEN SENTINEL-1 AND COSMO SKY-MED DATA FOR SNOW MONITORING IN ALPINE AREASen_US
dc.typeBook chapteren_US
dc.date.updated2020-04-01T16:30:00Z
dc.publication.titleIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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