Microwave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.
