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Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements
The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion m...
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Published in: | Water (Basel) 2020-01, Vol.12 (1), p.21 |
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description | The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data. |
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Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w12010021</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Coherent scattering ; Decomposition ; Distribution ; Environmental aspects ; Firn ; Forecasts and trends ; Glaciers ; Ground penetrating radar ; Heterogeneity ; Image acquisition ; Inversion ; Measurement ; Radar ; Radar polarimetry ; Root-mean-square errors ; Satellites ; Snow ; Snow depth ; Snowpack ; Synthetic aperture radar ; Testing</subject><ispartof>Water (Basel), 2020-01, Vol.12 (1), p.21</ispartof><rights>COPYRIGHT 2020 MDPI AG</rights><rights>2019 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/). 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Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.</description><subject>Algorithms</subject><subject>Coherent scattering</subject><subject>Decomposition</subject><subject>Distribution</subject><subject>Environmental aspects</subject><subject>Firn</subject><subject>Forecasts and trends</subject><subject>Glaciers</subject><subject>Ground penetrating radar</subject><subject>Heterogeneity</subject><subject>Image acquisition</subject><subject>Inversion</subject><subject>Measurement</subject><subject>Radar</subject><subject>Radar polarimetry</subject><subject>Root-mean-square errors</subject><subject>Satellites</subject><subject>Snow</subject><subject>Snow depth</subject><subject>Snowpack</subject><subject>Synthetic aperture radar</subject><subject>Testing</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpNkclOwzAQQCMEEgh64A8sceIQ8JI4ybFiKUhFRV24Ro49BkNiBzsF9Qf4bkyLEDOH2d9IM0lySvAFYxW-_CQUE4wp2UuOKC5YmmUZ2f_nHyajEF5xlKwqyxwfJV9zGLyBD9Eip9GiF4OJrrAKLaHrnY_Bk_BGNKY1wwYZixbWffZCvqFr6IcX5D7Ao0krpAEftvXIaoRXaBWMfUaTx_kWF9ESGuctoMfZdDGeowcQYe2hAzuEk-RAizbA6NceJ6vbm-XVXTqdTe6vxtNUMkaGlIJuKlqosihFUyimc45VIcsCF0W8AOUV4Rq40JRyoKrkMuNcca0qoRRTJTtOznbc3rv3NYShfnVrb-PKmuZ5vAqN6Nh1set6Fi3Uxmo3eCGjKuiMdBa0iflxSXBOcJZVceB8NyC9C8GDrntvOuE3NcH1z2vqv9ewb0lygBo</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Singh, Gulab</creator><creator>Lavrentiev, Ivan I.</creator><creator>Glazovsky, Andrey F.</creator><creator>Patil, Akshay</creator><creator>Mohanty, Shradha</creator><creator>Khromova, Tatiana E.</creator><creator>Nosenko, Gennady</creator><creator>Sosnovskiy, Aleksandr</creator><creator>Arigony-Neto, Jorge</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-6275-7517</orcidid><orcidid>https://orcid.org/0000-0002-7774-5997</orcidid><orcidid>https://orcid.org/0000-0001-5445-2107</orcidid><orcidid>https://orcid.org/0000-0003-4848-2064</orcidid></search><sort><creationdate>20200101</creationdate><title>Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements</title><author>Singh, Gulab ; Lavrentiev, Ivan I. ; Glazovsky, Andrey F. ; Patil, Akshay ; Mohanty, Shradha ; Khromova, Tatiana E. ; Nosenko, Gennady ; Sosnovskiy, Aleksandr ; Arigony-Neto, Jorge</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-2efb927d878ab7d3f560d7c8707733926916fe6af226e2d86c466d6fd9add3d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Coherent scattering</topic><topic>Decomposition</topic><topic>Distribution</topic><topic>Environmental aspects</topic><topic>Firn</topic><topic>Forecasts and trends</topic><topic>Glaciers</topic><topic>Ground penetrating radar</topic><topic>Heterogeneity</topic><topic>Image acquisition</topic><topic>Inversion</topic><topic>Measurement</topic><topic>Radar</topic><topic>Radar polarimetry</topic><topic>Root-mean-square errors</topic><topic>Satellites</topic><topic>Snow</topic><topic>Snow depth</topic><topic>Snowpack</topic><topic>Synthetic aperture radar</topic><topic>Testing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Singh, Gulab</creatorcontrib><creatorcontrib>Lavrentiev, Ivan I.</creatorcontrib><creatorcontrib>Glazovsky, Andrey F.</creatorcontrib><creatorcontrib>Patil, Akshay</creatorcontrib><creatorcontrib>Mohanty, Shradha</creatorcontrib><creatorcontrib>Khromova, Tatiana E.</creatorcontrib><creatorcontrib>Nosenko, Gennady</creatorcontrib><creatorcontrib>Sosnovskiy, Aleksandr</creatorcontrib><creatorcontrib>Arigony-Neto, Jorge</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singh, Gulab</au><au>Lavrentiev, Ivan I.</au><au>Glazovsky, Andrey F.</au><au>Patil, Akshay</au><au>Mohanty, Shradha</au><au>Khromova, Tatiana E.</au><au>Nosenko, Gennady</au><au>Sosnovskiy, Aleksandr</au><au>Arigony-Neto, Jorge</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements</atitle><jtitle>Water (Basel)</jtitle><date>2020-01-01</date><risdate>2020</risdate><volume>12</volume><issue>1</issue><spage>21</spage><pages>21-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w12010021</doi><orcidid>https://orcid.org/0000-0001-6275-7517</orcidid><orcidid>https://orcid.org/0000-0002-7774-5997</orcidid><orcidid>https://orcid.org/0000-0001-5445-2107</orcidid><orcidid>https://orcid.org/0000-0003-4848-2064</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Coherent scattering Decomposition Distribution Environmental aspects Firn Forecasts and trends Glaciers Ground penetrating radar Heterogeneity Image acquisition Inversion Measurement Radar Radar polarimetry Root-mean-square errors Satellites Snow Snow depth Snowpack Synthetic aperture radar Testing |
title | Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements |
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