This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

Pettinato, S., Santi, E., Paloscia, S., Baroni, F., Pilia, S., Santurri, L., et al. (2023). Multi-Frequency SAR Images for Investigations of the Cryosphere: Preliminary Results of Criosar Project. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium (pp.1585-1588). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS52108.2023.10281448].

Multi-Frequency SAR Images for Investigations of the Cryosphere: Preliminary Results of Criosar Project

Colombo R.;Bramati G.;Di Mauro B.;De Gregorio L.;Silvestri L.;
2023

Abstract

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.
paper
Artificial Neural Networks; LWC; Machine Learning; Multi-mission SAR; permafrost; rock glaciers; Snow depth; SWE;
English
IGARSS 2023- IEEE International Geoscience and Remote Sensing Symposium - 16-21 July 2023
2023
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
9798350320107
2023
2023-July
1585
1588
none
Pettinato, S., Santi, E., Paloscia, S., Baroni, F., Pilia, S., Santurri, L., et al. (2023). Multi-Frequency SAR Images for Investigations of the Cryosphere: Preliminary Results of Criosar Project. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium (pp.1585-1588). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS52108.2023.10281448].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/471181
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