SWE retrieval by exploiting COSMO-SkyMed X-band SAR imagery and ground data through a machine learning approach

Ludovica De Gregorio; Francesca Cigna; Giovanni Cuozzo; Alexander Jacob; Simonetta Paloscia; Simone Pettinato; Emanuele Santi; Deodato Tapete; Lorenzo Bruzzone; Claudia Notarnicola

doi:10.1117/12.2550824

8 October 2019 SWE retrieval by exploiting COSMO-SkyMed X-band SAR imagery and ground data through a machine learning approach

Ludovica De Gregorio, Francesca Cigna, Giovanni Cuozzo, Alexander Jacob, Simonetta Paloscia, Simone Pettinato, Emanuele Santi, Deodato Tapete, Lorenzo Bruzzone, Claudia Notarnicola

Author Affiliations +

Proceedings Volume 11154, Active and Passive Microwave Remote Sensing for Environmental Monitoring III; 111540M (2019) https://doi.org/10.1117/12.2550824
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting the information derived from X-band Synthetic Aperture Radar (SAR) imagery acquired by the Italian Space Agency COSMO-SkyMed satellite constellation in StripMap HIMAGE mode and manual SWE ground measurements. The idea is to verify the sensitivity of the backscattering coefficient at X-band to the SWE and, by means of a Support Vector Regression (SVR) algorithm, to estimate the SWE for the South Tyrol region, north-eastern Italy. The regressor is trained by exploiting about 1,000 simulated backscattering coefficients corresponding to different snowpack conditions, obtained with a theoretical model based on the Dense Media Radiative Transfer theory - Quasi-crystalline approximation Mie scattering of Sticky spheres (DMRT-QMS). Then, the performance is evaluated on the backscattering values derived from COSMO-SkyMed satellite images and using the corresponding ground measurements of SWE as references. The results show a correlation coefficient equal to 0.6, a bias of 10.5 mm and a RMSE of 51.8 mm between estimated SWE values and ground measurements. The limited performance could be related to the DMRT-QMS theoretical model used for the simulations that results to be very sensitive to snow grain size and may have generated a training dataset only partially representative of satellite derived backscattering coefficients used for testing the algorithm.

Citation Download Citation

Ludovica De Gregorio, Francesca Cigna, Giovanni Cuozzo, Alexander Jacob, Simonetta Paloscia, Simone Pettinato, Emanuele Santi, Deodato Tapete, Lorenzo Bruzzone, and Claudia Notarnicola "SWE retrieval by exploiting COSMO-SkyMed X-band SAR imagery and ground data through a machine learning approach", Proc. SPIE 11154, Active and Passive Microwave Remote Sensing for Environmental Monitoring III, 111540M (8 October 2019); https://doi.org/10.1117/12.2550824

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