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• W3080136306 abstract "Abstract Passive microwave datasets have been used to quantify the extent and duration of surface melt in Greenland and Antarctica from 1978 on with daily and near-daily intervals. These results have important implications for climate analysis and may help evaluate ice shelf stability. However, the accuracy of passive microwave methods used to detect melt is difficult to quantify, especially on the Antarctic Peninsula. Here four different melt detection methods are employed, including a new formulation of a statistical analysis of brightness temperature time series using a K-means clustering algorithm. Strikingly, two of the most widely used passive microwave melt detection methods are found to vary by 48% mean days of melt per year across six different locations on the Larsen C, Wilkins, and George VI Ice Shelves. In the absence of ground truth observations, time series of Sentinel-1 SAR observations from 2016 on provide a comparison dataset. In topographically flat regions where surface melt is spatially uniform, the passive microwave melt detection method based on a K-means analysis and the cross-polarization gradient ratio method demonstrate the highest agreement and correlation with active radar melt detection methods. One issue which has plagued passive microwave analysis is its coarse spatial resolution. High resolution SAR images are able to demonstrate and quantify the spatial variability of melt within individual passive microwave pixels. Melt is shown to be suitably uniform in space for passive microwave applications at the study sites on Antarctic Peninsula ice shelves, but not so in other regions of the Antarctic Peninsula. Spatial heterogeneity of surface melt on the sub-pixel scale is often related to varying surface topography." @default.
• W3080136306 created "2020-09-01" @default.
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• W3080136306 date "2020-12-01" @default.
• W3080136306 modified "2023-10-13" @default.
• W3080136306 title "Evaluation of passive microwave melt detection methods on Antarctic Peninsula ice shelves using time series of Sentinel-1 SAR" @default.
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• W3080136306 doi "https://doi.org/10.1016/j.rse.2020.112044" @default.
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