###### Abstract  
Claiers in High Mountain Asia (fMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainotanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers. r 28 April 2021 29 June 2021 29 June 2021 29 July 2021 29 June 2021 29 June 2021 29 June 2021 29 July  
Mountain glaciers are located in high-altitude remote mountainous areas, which have complex terrains [16; 17; 18; 19]. Since these glaciers are of various types, traditional ground survey methods cannot adequately track large-scale, periodic, systematic, and comprehensive glacier changes. A technique to effectively measure and monitor mountain glaciers is crucial at this stage [16; 17; 18]. However, in high mountain areas, which experience frequent cloud activity and have a large amount of debris coverage, automatic and semi-automatic glacier mapping based on remote sensing images is challenging [1; 11; 20], and short-term studies such as these do not necessarily show stable changes over time. Debris-covered glaciers are a special type of glacier [21; 22]. The surfaces of mountain glaciers are frequently covered in supraglacial debris, whose spectral characteristics are similar to those of rocks and soil at the foot of the mountain [21; 23], making them hard to distinguish from rock glaciers and periglacial areas [24]. Particularly in the Tibetan Plateau, owing to the influence of the monsoon climate, frequent cloud activity occurs during the glacier melting period. This, in combination with topographical cloud influences [25], makes it very difficult to obtain high-quality cloud-free images. Seasonal snow cover and debris also increase the difficulty and uncertainty of glacier boundary identification [25; 26]. These uncertain factors cause significant interference in research on glacier distribution, water storage, glacier changes, hydrological modeling, and information extraction on the local and regional scales, which is the focus [27] of current research on glaciers [21]. By combining machine learning methods to analyze and extract features based on the different dimensions of the glacier remote sensing images in each pixel, a rapid automatic classification of glaciers can be achieved, clearly reflecting the various glacier types [27; 28].