| Summary: | Surface snowmelt on Antarctic ice sheets is not only an important indicator of global climate change but also a key controllingfactor of the global climate. The QuikSCAT microwave scatterometer is highly sensitive to the liquid water content of snow, and it can beused macroscopically, rapidly, objectively, and effectively to monitor and assess Antarctic snowmelt condition. Based on a long time seriesof QuikSCAT data, a new automatic threshold segmentation method was proposed in this study for the detection of Antarctic snowmeltonset date, end date, and duration. The method takes multi-scale edge information extracted from backscattering coefficients by wavelettransform, and a generalized Gaussian model automatically fits the optimal wet and dry snow classification threshold. The method, whichdoes not rely on measured data, inherits and develops the advantage of snowmelt detection and it achieves the goal of an effective Antarcticsnowmelt monitoring system. Comparison of the snowmelt results with temperatures at eight automatic weather stations demonstrated thefeasibility of using scatterometer data with the proposed algorithm for Antarctic snowmelt detection.
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