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The radiogenic isotope compositions of strontium (Sr) and neodymium (Nd) on the surface of the Earth are powerful tools for tracing dust sources and sinks on Earth's surface. To differentiate between the spatial variabilities of aeolian dust sources in key cryospheric regions at the three poles (including the 'Third Pole' covering the high mountainous area in Asia, the Arctic and Antarctica), a dataset of the Sr-Nd isotopic compositions from the terrestrial extremely cold or arid environments in this study was compiled, similar to the method of Blanchet (2019). The database identified snow, ice, sand, soil (loess) and sediment from the modern dust samples and paleoclimatic records of the three poles based on 43 different references, with a total of 967 data points. There are 274 data points from the third pole, 302 data points from the Arctic, and 391 data points from Antarctica. The sampling and measurement methods and the quality of these data are recognized and introduced. For each pole, geographical coordinates and other information are provided. The main scientific purpose of this dataset is to provide our own measurements and collect documentation for the Sr-Nd dataset, which will be useful for determining the sources and transport pathways of dust at the three poles and to investigate whether multiple dust sources are present at each of the poles. This dataset provides exhaustive detailed documentation of the isotopic signatures at the three poles during specific time intervals, which are useful for understanding the dust sources or sinks of the three poles. The datasets are available from the National Tibetan Plateau Data Center (https://doi.org/10.11888/Cryos.tpdc.272100, Du et al., 2022). [ABSTRACT FROM AUTHOR]

Previously, detecting surface snowmelt from passive microwave remote sensing data based on thresholding used arbitrarily selected cut-off values. To avoid the problems of the arbitrary threshold, this letter aims to find optimal threshold values through automated calibration. By using a wavelet-transform-based method and an interactive visualization and analysis software tool, 6-year snowmelt data were derived as reference for the calibration. The optimal threshold value is determined by minimizing the melt index error. This study shows that the previously used threshold value tends to systematically underestimate the surface melt. The spatial and temporal variability of the threshold value is examined. Although the optimal threshold value varies from region to region in the Antarctic Ice Sheet, it is relatively consistent from year to year, which implies that the optimally determined threshold value for each region can be extended to other years. [ABSTRACT FROM AUTHOR]