## 2 Research Area and Data Sources
### Dataset Acquisition and Pre-Processing  
Summer is usually the best season to identify glaciers from images because most seasonal snow melts during this season. However, for the HMA area, owing to the differences in climate and topography between the various basins, the acquisition time of the images cannot be generalized [60]. With the help of historical Google Earth images, we selected images with as little cloud coverage and seasonal snow as possible from the images that match the best seasons as our final data set, as detailed in Table 1. Additionally, Landsat 8 scenes of World Reference System 2 both used for Eastern Pamir (path 149 and row 33) and Nyainqentanglha (path 138 and row 39) can be freely downloaded  
Figure 1: Typical topographic features in High Mountain Asia and an overview of the glacier research areas. from [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), (accessed on 2 July 2021) and the Geospatial Data Cloud site ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn))), (accessed on 2 July 2021) for ASTER GDEM V2 data. The second glacier inventory dataset for China was obtained from the Institute of Tibetan Plateau Research, the Chinese Academy of Sciences. Further, the Randolph Glacier Inventory is available from GLIMS ([[https://www.glims.org/](https://www.glims.org/)]([https://www.glims.org/](https://www.glims.org/))), (accessed on 2 July 2021). A dataset of the glacier inventory of West China in 2018 is available from [[http://www.csdata.org/p/551/](http://www.csdata.org/p/551/)]([http://www.csdata.org/p/551/](http://www.csdata.org/p/551/)), (accessed on 2 July 2021). These inventory datasets were applied to estimate the effectiveness of the glacier boundary detection, based on the RF-CNN algorithm. Before the classification of glaciers, the original remote sensing data must be pre-processed. The specific steps of this process include the following:  
1. Perform radiometric and atmospheric correction on the spectral data of Landsat 8 images. 2. The band calculation tool of the ENVI software is used to normalize the data of each band, and the normalized difference vegetation index (NDVI) [61; 62], normalized difference water index (NDVI) [63; 64], and normalized difference snow index (NDSI) [26; 65] are calculated. It also identifies the coastal/aerosol, red, green, and blue bands, along with the near-infrared (NIR) and mid-infrared (SWIR-1 and SWIR-2) bands. The texture features (mean) [11] based on spectral data are then calculated by the statistical method of the grey-scale co-occurrence matrix (GLCM) proposed in the early 1970s by [PERSON] et al. [66]. Temperature characteristics (LST) are automatically obtained by identifying image metadata information through the surface temperature inversion tool of the ENVI software and obtaining atmospheric profile parameters. The glacier movement data are obtained by tracking the displacement of the glacier surface features between the two phases of Landsat 8 images based on the image cross-correlation method of frequency domain transformation in the COSI-Corr software package [67]. The calculation of terrain features is based on DEM data to obtain the elevation, slope, aspect, and shaded relief. The above feature data (spectral features + textural features + temperature features + topographic features + movement velocity features) are separately output as a grayscale image, which results in 23 grayscale images. 3. Load the 23 grayscale images and create a new debris-covered glacier vector file according to the visual interpretation method based on 23 grayscale images showing