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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Online Access: | http://hdl.handle.net/1828/12218 https://doi.org/10.3390/rs10101603 |
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ftuvicpubl:oai:dspace.library.uvic.ca:1828/12218 2023-05-15T15:18:32+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 Ramjan, Saroat Geldsetzer, Torsten Scharien, Randall Yackel, John 2018 application/pdf http://hdl.handle.net/1828/12218 https://doi.org/10.3390/rs10101603 en eng Remote Sensing Ramjan, S., Geldsetzer, T., Scharien, R., & Yackel, J. (2018). Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters. Remote Sensing. 10(10), 1-21. https://doi.org/10.3390/rs10101603. https://doi.org/10.3390/rs10101603 http://hdl.handle.net/1828/12218 melt pond fraction snow SAR polarimetric parameters GLCM texture Article 2018 ftuvicpubl https://doi.org/10.3390/rs10101603 2022-05-19T06:10:54Z 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 R2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R2 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. The authors would like to thank the participants of the Arctic-ICE 2012 Field Experiment based out of Resolute Bay, Nunavut, Canada. We would like to thank all team members for their support and hard work in the field program, including Principal Investigators, C.J. Mundy (CEOS, University of Manitoba) and B. Else (University of Calgary). We highly appreciate M.M. Rahman (University of Calgary) for his assistance in GLCM texture analysis. The authors would like to thank for the valuable comments provided by the reviewers; their contributions have substantially improved this paper. We also appreciate the collegial assistance from M. Mahmud and V. Nandan. We extend our ... Article in Journal/Newspaper Arctic Nunavut Resolute Bay Sea ice University of Victoria (Canada): UVicDSpace Arctic Canada Nunavut Resolute Bay ENVELOPE(-94.842,-94.842,74.677,74.677) Resolute Passage ENVELOPE(-95.585,-95.585,74.702,74.702) Remote Sensing 10 10 1603 |
institution |
Open Polar |
collection |
University of Victoria (Canada): UVicDSpace |
op_collection_id |
ftuvicpubl |
language |
English |
topic |
melt pond fraction snow SAR polarimetric parameters GLCM texture |
spellingShingle |
melt pond fraction snow SAR polarimetric parameters GLCM texture Ramjan, Saroat Geldsetzer, Torsten Scharien, Randall Yackel, John 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 R2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R2 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. The authors would like to thank the participants of the Arctic-ICE 2012 Field Experiment based out of Resolute Bay, Nunavut, Canada. We would like to thank all team members for their support and hard work in the field program, including Principal Investigators, C.J. Mundy (CEOS, University of Manitoba) and B. Else (University of Calgary). We highly appreciate M.M. Rahman (University of Calgary) for his assistance in GLCM texture analysis. The authors would like to thank for the valuable comments provided by the reviewers; their contributions have substantially improved this paper. We also appreciate the collegial assistance from M. Mahmud and V. Nandan. We extend our ... |
format |
Article in Journal/Newspaper |
author |
Ramjan, Saroat Geldsetzer, Torsten Scharien, Randall Yackel, John |
author_facet |
Ramjan, Saroat Geldsetzer, Torsten Scharien, Randall Yackel, John |
author_sort |
Ramjan, Saroat |
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 |
Remote Sensing |
publishDate |
2018 |
url |
http://hdl.handle.net/1828/12218 https://doi.org/10.3390/rs10101603 |
long_lat |
ENVELOPE(-94.842,-94.842,74.677,74.677) ENVELOPE(-95.585,-95.585,74.702,74.702) |
geographic |
Arctic Canada Nunavut Resolute Bay Resolute Passage |
geographic_facet |
Arctic Canada Nunavut Resolute Bay Resolute Passage |
genre |
Arctic Nunavut Resolute Bay Sea ice |
genre_facet |
Arctic Nunavut Resolute Bay Sea ice |
op_relation |
Ramjan, S., Geldsetzer, T., Scharien, R., & Yackel, J. (2018). Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters. Remote Sensing. 10(10), 1-21. https://doi.org/10.3390/rs10101603. https://doi.org/10.3390/rs10101603 http://hdl.handle.net/1828/12218 |
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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1766348727357800448 |