## 2 Research Area and Data Sources
### Input Feature Analysis for Classification  
The velocity of glacier movement is a key factor for understanding the nature of glaciers and quantifying glacier changes, and it is also an important metric for glacier dynamics [4]. A frequency domain algorithm was used in Landsat 8 OLI images (panchromatic band, 15 m resolution) taken approximately one year apart between the two periods to calculate the correlation coefficient in the COSI-Corr software, with the reference window set to 128 and the search window set to 32, to obtain the glacier movement velocity data (Figure 2). Figure 2 shows the velocity for debris-covered glaciers in the study area. The Karayaalak Glacier conforms to the characteristics of a fast-moving glacier, where the lateral branches move fast (particularly the western) while the main glacier trunk has a much lower speed [68], which is mainly due to the many small supraglacial lakes in the east branch of the Karayaalak Glacier (Figure 3). It is generally believed that the supraglacial lakes accelerate glacier movement [15]. The surface velocity of the tongue part of the Qimgan Glacier does not conform to the characteristics of normal glacier flow. According to Google Earth images and a study conducted by [PERSON] [69], this area is a dead ice area covered in surface debris (Figure 3), indicating that the deep surface debris-covered area may no longer be active. It should be noted that the feature tracking of steep-slope areas is affected by their topography and other factors, leading to significant uncertainty. In summary, although the glacier movement velocity is useful to ascertain the surface debris area since the glacier movement velocity does not conform to a uniform law, we also need to refer to other variables to fully identify and extract debris-covered glacier areas. Figure 2: Analysis of velocity for debris-covered glaciers. (**a**) Eastern Pamir and (**b**) Nyainotanglha. Figure 3: Typical distribution of debris in the study area. The background is Google Earth images; the red line is our vectorized debris-covered glacier vector file. (**a**) detail view of the Karayaalak glacier; (**b**) detail view of the Qimgan glacier and (**c**) detail view of the Xibu glacier. The correlations shown in Figure 4 are the correlation coefficients of the variables for the classification of debris-covered glaciers. When the correlation coefficient \(p\) is greater than 0.05, the relationship between the two is strong. According to an analysis by [PERSON] (2018), the mean in image texture features plays a decisive role in the mapping of glaciers. Therefore, to avoid model overfitting, only the mean is used here to represent texture features. According to saliency relationships, it can be seen in Figure 4 that--among the classification variables of debris-covered glaciers--SWIR1, SWIR2, and their respective mean values have a strong influence. Further, NDVI, NDSI, and LST also play important roles. This is primarily because the optical band plays a crucial role in all variables, and the exponential features and texture features generated by it respond to it simultaneously. Terrain features and glacier movement velocity features are equally important and play an auxiliary role in the classification of debris-covered glaciers. The importance of these two types of features takes into account the control of topographic factors, so they are also related. We calculated the eigenvector value relating to the feature vector, which represents the contribution of each variable to the debris-covered glacier classification. We observe that the eigenvalues of almost all variables are positive. The greater the positive value of strong reflection, the better the effect of strong absorption. This is in line with the requirements of debris-covered glacier classification. Figure 4: Analysis of the correlation of variables for the classification of debris-covered glaciers. The colors and shapes in the graph represent the magnitude of the correlation coefficient.