DERIVATION OF SUPRAGLACIAL DEBRIS COVER BY MACHINE LEARNING ALGORITHMS ON THE GEE PLATFORM: A CASE STUDY OF GLACIERS IN THE HUNZA VALLEY

Abstract

Calculating the spatial-temporal distribution of supraglacial debris cover on glaciers is essential for understanding mass balance processes, glacier lake outburst floods, hydrological predictions, and glacier fluctuations that have attracted attention in recent years. However, due to the reflectance of supraglacial debris is similar to that of non-glacier slopes, mapping supraglacial debris cover based on optical remote sensing remains challenging. In this paper, we used NDSI and machine learning algorithm to extract debris cover on glaciers in Hunza Valley, Pakistan. Our result showed that the RF model has the best classification accuracy with kappa coefficient of 0.94 and overall accuracy of 96%. The debris-covered area increased by 21.31% from 1990 to 2019 (394.76 km2 – 478.88 km2) in the study area. Results and the method are of significance in the assessment of meltwater modeling for glaciers with debris cover.