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Remote Sens. 2018, 10(10), 1603; https://doi.org/10.3390/rs10101603

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

1
Cryosphere Climate Research Group, Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of Geography, University of Victoria, Victoria, BC V8W 2Y2, Canada
*
Author to whom correspondence should be addressed.
Received: 28 June 2018 / Revised: 9 September 2018 / Accepted: 2 October 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Cryospheric Remote Sensing II)
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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 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. View Full-Text
Keywords: melt pond fraction; snow; SAR; polarimetric parameters; GLCM texture melt pond fraction; snow; SAR; polarimetric parameters; GLCM texture
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ramjan, S.; Geldsetzer, T.; Scharien, R.; Yackel, J. 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 Sens. 2018, 10, 1603.

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