Abstract
ABSTRACT This study presents an XPGR coupled with an improved ant colony algorithm to automatically detect the Antarctic ice sheet surface snowmelt and acquire high-precision snowmelt information. This approach first enlarges the difference between dry snow and wet snow using the XPGR algorithm and then utilizes an enhanced ant colony algorithm to adaptively find the best threshold for segmenting dry snow and wet snow. The dissimilarity matrix, which determines the initial clustering centre and utilizes Levy flight to modify the clustering radius dynamically, is a major advancement to the ant colony algorithm. The method proposed in this study was compared to the standard XPGR algorithm from October 2017 to February 2018 and from October 2019 to February 2020 to evaluate the practicality and rationale of the proposed method. The further verification of six automatic weather stations (AWS) shows that the method proposed in this study has higher accuracy.
Published Version
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