Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters
Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RAD...
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ftmdpi:oai:mdpi.com:/2072-4292/10/10/1603/ 2023-08-20T04:08:51+02:00 Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters Saroat Ramjan Torsten Geldsetzer Randall Scharien John Yackel 2018-10-09 application/pdf https://doi.org/10.3390/rs10101603 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10101603 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 10; Pages: 1603 melt pond fraction snow SAR polarimetric parameters GLCM texture Text 2018 ftmdpi https://doi.org/10.3390/rs10101603 2023-07-31T21:46:09Z Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover. Text Nunavut Sea ice MDPI Open Access Publishing Nunavut Canada Resolute Passage ENVELOPE(-95.585,-95.585,74.702,74.702) Remote Sensing 10 10 1603 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
melt pond fraction snow SAR polarimetric parameters GLCM texture |
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melt pond fraction snow SAR polarimetric parameters GLCM texture Saroat Ramjan Torsten Geldsetzer Randall Scharien John Yackel Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
topic_facet |
melt pond fraction snow SAR polarimetric parameters GLCM texture |
description |
Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover. |
format |
Text |
author |
Saroat Ramjan Torsten Geldsetzer Randall Scharien John Yackel |
author_facet |
Saroat Ramjan Torsten Geldsetzer Randall Scharien John Yackel |
author_sort |
Saroat Ramjan |
title |
Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
title_short |
Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
title_full |
Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
title_fullStr |
Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
title_full_unstemmed |
Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters |
title_sort |
predicting melt pond fraction on landfast snow covered first year sea ice from winter c-band sar backscatter utilizing linear, polarimetric and texture parameters |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10101603 |
long_lat |
ENVELOPE(-95.585,-95.585,74.702,74.702) |
geographic |
Nunavut Canada Resolute Passage |
geographic_facet |
Nunavut Canada Resolute Passage |
genre |
Nunavut Sea ice |
genre_facet |
Nunavut Sea ice |
op_source |
Remote Sensing; Volume 10; Issue 10; Pages: 1603 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10101603 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10101603 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
10 |
container_start_page |
1603 |
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1774721395652034560 |