Radar backscattering changes in Arctic sea ice from late summer to early autumn observed by space-borne X-band HH-polarization SAR

Abstract

Melt ponds are believed to play an important role in sea ice dynamics because they accelerate the melting of sea ice in the warmer spring and summer months. Additionally, they are known to absorb solar radiation rather than reflect it as the surrounding sea ice does. However, the size and distribution of melt ponds are highly variable, and thus, the contribution of melt ponds to sea ice melting should differ based on the maturity of the melt pond. Because of the harsh conditions of the Arctic, estimating the actual surface changes via in situ measurements and/or optical remote sensing data is difficult. In this study, we present a high-resolution time series analysis of the short-term variation of sea ice and melt ponds over the Beaufort Sea using space-borne multispectral and synthetic aperture radar (SAR) images. A KOMPSAT-3 optical image was used for an initial classification of the surface types, and 15 TerraSAR-X SAR images covering 46 days in the 2014 Arctic summer were used to perform a dense time-series analysis. The surface of the target sea ice was classified into 6 categories based on spectral characteristics. The temporal variation of the radar backscattering coefficient in each class exhibited a distinct pattern, which was closely related to surface changes. Overall, changes in the radar backscattering coefficient indicated dynamic surface changes, except over pressure ridges. All ice classes showed a two-step decrease in radar backscattering, whereas snow-covered ice surfaces exhibited far fewer changes compared to bare ice surfaces. The surfaces adjacent to ponds showed stronger negative decreases than other classes. The changes in dark melt pond classes presented a complex non-linear decrease, which differed from the stepwise decrease of blue melt ponds. These observations can be used for important modeling studies of surface melting/freezing rates and to infer the variation over large areas using remote sensing data.