Abstract: Snowmelt in Antarctica has considerable impact on sea level rise and climate change. We investigated the detection of snowmelt on the Antarctic Peninsula using C-band spaceborne synthetic aperture radar imagery. Based on an analysis of the backscatter characteristics of dry, percolation, and wet snow, we used a decision tree classification to divide the ice sheet into zones. The statistical analysis demonstrated that the backscatter coefficients of snow zones, especially the wet snow zone, depend mainly upon melt level and do not have a centralized distribution. The wet snow zone in drastic melt is too similar to the dry snow zone to be distinguished using the backscatter coefficient alone. Therefore, we introduced the dry snow distribution and elevation into the classification, and compared the two decision tree methods. We verified the detection results using microwave radiometer and automatic weather station data. The results showed that the two presented decision tree classifications, derived from Radarsat-2 dual-pol data, were both efficient in determining glacier zone division and distinguishing snowmelt status and thus, are shown capable of achieving high-resolution snowmelt detection in Antarctica.

Key words: Radarsat-2, dual-pol, ScanSAR, Antarctica, snowmelt, decision tree classification