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AbstractUtilizing synthetic aperture radar (SAR) data to understand and map sea ice roughness is an active area of research. An object-based image analysis is here used to quantitatively link the winter first-year sea ice surface roughness to C-band RADARSAT-2 and L-band ALOS-2 PALSAR-2 SAR backscatter measured in the Canadian Arctic. Results indicate: (1) C-band HH-polarization backscatter is correlated with roughness (r = 0.86) at a shallow incidence angle; and (2) L-band HH- and VV-polarization backscatter is correlated with roughness (r = 0.82) at a shallow incidence angle. Retrieval models for surface roughness are developed and applied to the imagery to demonstrate the potential of SAR for mapping deformation with a best case RMSE of 5 mm. Additionally, strong correlations between C- and L-band SAR backscatter acquired during the advanced melt period, and the winter measured surface roughness, are observed. These results are discussed in the context of SAR potentially providing significant sea ice structural information despite the occurrence of melt and presence of surface melt ponds.

Spring melt pond fraction (fp) has been shown to influence September sea ice extent and, with a growing need to improve melt pond physics in climate and forecast models, observations at large spatial scales are needed. We present a novel technique for estimating fpon sea ice at high spatial resolution from the Sentinel‐1 satellite during the winter period leading up to spring melt. A strong correlation (r= −0.85) is found between winter radar backscatter and fpfrom first‐year and multiyear sea ice data collected in the Canadian Arctic Archipelago (CAA) in 2015. Observations made in the CAA in 2016 are used to validate a fpretrieval algorithm, and a fpprediction for the CAA in 2017 is made. The method is effective using the horizontal transmit and receive polarization channel only and shows promise for providing seasonal, pan‐Arctic, fpmaps for improved understanding of melt pond distributions and forecast model skill. Recent and well‐documented changes in Arctic sea ice have introduced the need for timely and accurate seasonal forecasts of ice conditions. Seasonal forecasts of ice conditions will reduce the risks to humans and help preserve the fragile Arctic ecosystem by preventing accidents and spills. Recent studies have shown a link between the amount of surface meltwater flooding that occurs on sea ice in the spring, termed melt pond fraction, and the extent of sea ice that remains at the end of summer. This link is due to the ability of surface meltwater to absorb more sunlight compared to bare ice and snow. This study provides a new way to estimate the amount of surface meltwater flooding expected to occur on the sea ice in spring, using satellite data collected during the winter period. The results presented here provide a key link between winter and late summer sea ice conditions that will enhance the ability of forecasters to make accurate seasonal predictions several months in advance of the active summer period. A novel approach to estimate spring melt pond fraction from winter Sentinel‐1 backscatter is developedThe correlation between backscatter and melt pond fraction is strong at r= −0.85Large‐scale maps of predicted spring melt pond fraction months in advance are possible