SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques

E Santi; S Paloscia; S Pettinato; Ludovica De Gregorio; Giovanni Cuozzo; Alexander Jacob; Claudia Notarnicola; F Cigna; D Tapete

doi:10.23919/URSIGASS49373.2020.9232247

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SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques

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SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques

E Santi, S Paloscia, S Pettinato, Ludovica De Gregorio, Giovanni Cuozzo, Alexander Jacob, Claudia Notarnicola, F Cigna and D Tapete

Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 (Rome, 29/08/2020 - 05/09/2020)

2020

DOI: https://doi.org/10.23919/URSIGASS49373.2020.9232247

Handle:

https://hdl.handle.net/10863/19472

Abstract

The potential of satellite Synthetic Aperture Radar (SAR) sensors for Snow Water Equivalent (SWE) retrieval in Alpine areas is assessed in this study. X-band HH-polarized SAR backscatter from 2012-2015 images acquired by the COSMO-SkyMed constellation over the South Tyrol province in northern Italy is compared with SWE in-situ measurements and nivo-meteorological station records. The resulting relationship is compared with simulations based on the Dense Media Radiative Transfer – Quasi Mie Scattering (DMRT – QMS) model. Artificial Neural Networks (ANN) and Support Vector Regression (SVR) machine learning techniques are trained and used for SWE retrieval from COSMO-SkyMed data. Good accuracy and small computational cost are observed for both ANN and SVR. The resulting SWE maps agree with snow conditions measured in-situ.

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Details

Title: SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques
Creators: E Santi
S Paloscia
S Pettinato
Ludovica De Gregorio
Giovanni Cuozzo
Alexander Jacob
Claudia Notarnicola
F Cigna
D Tapete
Conference: Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 (Rome, 29/08/2020 - 05/09/2020)
Number of pages: 4
Identifiers: (EURAC)23434544
991006127681801241
Copyright: Due to the COVID-19 pandemic, the URSI Board, together with the GASS 2020 Local Organizing Committee decided to postpone the URSI 2020 General Assembly and Scientific Symposium by 1 year. However, all authors of accepted papers were offered the possibility to either withdrawn their paper or to publish their work in the GASS 2020 Proceedings. The papers of the authors who have chosen to include their work in these URSI GASS 2020 Proceedings are listed on this and the following webpages. All submitted PDFs are included, but for some papers also a slide-presentation or video-presentation is available. This is in line with the directions set out by IEEE for submitting papers of the URSI GASS 2020 to IEEE Xplore or in line with the requiremnets for the URSI Young Scientists Awards and the Student Paper Competition.
Academic Unit: Institute for Earth Observation
Language: English
Resource Type: Conference proceeding
Local Fields: Scientific
Author Names String: Santi E, Paloscia S, Pettinato S, De Gregorio L, Cuozzo G, Jacob A, Notarnicola C, Cigna F, Tapete D

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