In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve f...

Temporal and spatial soil moisture dynamics in mountain meadows by integrating Radarsat 2 images and ground data

Pasolli, Luca;Castelletti, Davide;Bruzzone, Lorenzo;
2014-01-01

Abstract

In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve f...
2014
2014 IEEE Geoscience and Remote Sensing Symposium
USA
Institute of Electrical and Electronics Engineers Inc.
9781479957750
C., Notarnicola; Pasolli, Luca; G., Cuozzo; F., Greifeneder; G., Bertoldi; S., Della Chiesa; G., Niedrist; Castelletti, Davide; U., Tappeiner; Bruzzon...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/101173
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