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COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (La...

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Autores principales: Paloscia, Simonetta, Pettinato, Simone, Santi, Emanuele, Valt, Mauro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298657/
https://www.ncbi.nlm.nih.gov/pubmed/28054962
http://dx.doi.org/10.3390/s17010084
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author Paloscia, Simonetta
Pettinato, Simone
Santi, Emanuele
Valt, Mauro
author_facet Paloscia, Simonetta
Pettinato, Simone
Santi, Emanuele
Valt, Mauro
author_sort Paloscia, Simonetta
collection PubMed
description In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R(2) = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05.
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spelling pubmed-52986572017-02-10 COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment Paloscia, Simonetta Pettinato, Simone Santi, Emanuele Valt, Mauro Sensors (Basel) Article In this work, X band images acquired by COSMO-SkyMed (CSK) on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR) images (Landsat-8 and CSK) in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R(2) = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05. MDPI 2017-01-04 /pmc/articles/PMC5298657/ /pubmed/28054962 http://dx.doi.org/10.3390/s17010084 Text en © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paloscia, Simonetta
Pettinato, Simone
Santi, Emanuele
Valt, Mauro
COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title_full COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title_fullStr COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title_full_unstemmed COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title_short COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment
title_sort cosmo-skymed image investigation of snow features in alpine environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298657/
https://www.ncbi.nlm.nih.gov/pubmed/28054962
http://dx.doi.org/10.3390/s17010084
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