Virtual Constellation of X-C And L Band SAR Images to Assess Soil And Vegetation Water Content in Agricultural Areas

The present work exploits multi-frequency SAR and optical imagery in order to assess soil and vegetation water content values in agricultural areas in Italy and Argentina. Sentinel-1, Sentinel-2, ALOS-2, RADARSAT-2, and COSMO-SkyMed imagery were used in a retrieval algorithm based on Support Vector...

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Bibliographic Details
Main Authors: Cuozzo, G., Greifeneder, F., Padovano, A., Solorza, R., Bertoldi, G., Notarnicola, C.
Format: Conference Proceeding
Language:English
Subjects:
agriculture
Backscatter
machine learning
multi-frequency
Optical imaging
Optical sensors
satellite
Soil moisture
Synthetic aperture radar
Vegetation mapping
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Summary:The present work exploits multi-frequency SAR and optical imagery in order to assess soil and vegetation water content values in agricultural areas in Italy and Argentina. Sentinel-1, Sentinel-2, ALOS-2, RADARSAT-2, and COSMO-SkyMed imagery were used in a retrieval algorithm based on Support Vector Machine (SVR) approach. Preliminary results indicate that the integration of optical and SAR data (C and L band) by the mean of machine learning techniques leads to an accurate retrieval of soil moisture (RMSE = 4%). Moreover, a preliminary test for the retrieval of Vegetation Water Content (VWC) indicates that ALOS-2 L-Band backscattering, combined with L-Band simulated backscattering, leads to a reliable model to compute VWC, without any optical information.
ISSN:2153-7003
DOI:10.1109/IGARSS.2019.8900126