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

Date

2018

Authors

Ramjan, Saroat
Geldsetzer, Torsten
Scharien, Randall
Yackel, John

Journal Title

Journal ISSN

Volume Title

Publisher

Remote Sensing

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.

Description

Keywords

melt pond fraction, snow, SAR, polarimetric parameters, GLCM texture

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.