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Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

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dc.contributor.author Ramjan, Saroat
dc.contributor.author Geldsetzer, Torsten
dc.contributor.author Scharien, Randall
dc.contributor.author Yackel, John
dc.date.accessioned 2020-10-19T22:18:01Z
dc.date.available 2020-10-19T22:18:01Z
dc.date.copyright 2018 en_US
dc.date.issued 2018
dc.identifier.citation 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. en_US
dc.identifier.uri https://doi.org/10.3390/rs10101603
dc.identifier.uri http://hdl.handle.net/1828/12218
dc.description.abstract 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. en_US
dc.description.sponsorship 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 sincere thanks to the Polar Continental Shelf Project and MEOPAR (Marine Environmental Observation Prediction and Response Network) for their funding and logistical support. We acknowledge Canadian NSERC Discovery grants to R. Scharien and J. Yackel towards this work. The Canadian Ice Service support in providing, planning and ordering RADARSAT-2 imagery is appreciated. Supplementary RADARSAT-2 imagery and planning support was provided by the Canadian Space Agency’s Science and Operational Applications Research program. RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. 2012. All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency. This research was funded by Canadian NSERC Discovery grants to John Yackel and Randy Scharien as well as MEOPAR (Marine Environmental Observation Prediction and Response Network) funding to Randy Scharien. The APC was funded by Canadian NSERC Discovery grants to John Yackel. en_US
dc.language.iso en en_US
dc.publisher Remote Sensing en_US
dc.subject melt pond fraction en_US
dc.subject snow en_US
dc.subject SAR en_US
dc.subject polarimetric parameters en_US
dc.subject GLCM texture en_US
dc.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 en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US


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