## 5 Discussion  
### Comparison with Existing Methods and Inventories  
We use the three kinds of glacier inventory data (CGI 2, RGI 6.0, and WCGI 2018) as the analysis object of the experimental results. Firstly, the scopes of the two types of glacier inventory data (RGI 6.0 and CGI2) are nearly the same in Eastern Pamir and Nyainqentanglha, so we primarily use RGI 6.0 for visual analysis, which considers the reference value of RGI in worldwide cryosphere research. According to statistics, WCGI2018 shows 461.448 km\({}^{2}\) of debris-covered glacier area in Eastern Pamir, but 462.521 km\({}^{2}\) of debris-covered glacier is found in RGI 6.0 and CGI2. The area of these debris-covered glaciers--for the most part--does not change; only a few glaciers exhibit slight changes (an area of less than 3 km\({}^{2}\)) owing to the greater response of large glaciers to the climate and terrain [1,60]. Further, WCGI2018 shows 6.128 km\({}^{2}\) of debris-covered glacier in Nyainqentanglha, but 6.197 km\({}^{2}\) of debris-covered glacier is found in RGI 6.0 and CGI2. Considering that WCGI2018 is also a manually drawn glacier contour based on CGI2, there is not much difference in terms of value; any variations could be due to human error caused by technical personnel's subjective understanding of glaciers. Secondly, when we conduct quantitative analysis, we randomly select 19 debris-covered glaciers as the research object (Table 6). We used an evaluation tool named the 'round robin' method, proposed by [PERSON] [78], to compare the area of debris-covered glaciers from RGI 6.0, CGI 2, and WCGI 2018 data with our RF-CNN results, taking into consideration that there are objective deviations in the judgment criteria of the three types of glacier inventory data. Finally, the average areas of the above three inventories are calculated as the criteria for evaluating the RF-CNN model, and the standard deviation of the glacier area is used as an indicator to measure the change in glacier area. In general, the area of the glacier trained by the RF-CNN model is larger than the area of the glacier inventory data of the above three datasets. The closer the standard deviation of the glacier area is to 0, the less the change in glacier area; this trend often appears in the small glaciers evaluated in Table 6. This is because large glaciers respond more severely to the combined effect of climate and topography than small glaciers [7,69]. Thirdly, taking Eastern Pamir as an example (Figure 13), the glacier boundary of RGI 6.0 is used as the visual judgment standard, and a buffer of 100 m is used as the error interval to identify the accuracy of RF-CNN for glacier extraction. We select four debris-covered glaciers (the Karayaalak Glacier, CN5Y663C0013, CN5Y663C0015, and the Qimgan Glacier) to demonstrate the performance of the RF-CNN model. The results obtained in this paper are all within the range of the buffer--that is, within the range of error. Regarding the Karayaalak Glacier, it is mentioned in the literature that there is no advancement at the terminus [68], which is consistent with our results. The CN5Y663C0013 and CN5Y663C0015 glaciers have significant glacier surges, which closely matches the results of WCGI 2018. This also implies that glacier extraction based on machine learning can support research into glacier advancement. The key problem when analyzing the Qimgan Glacier is the detection of the active and inactive debris-covered glaciers. Figure 3 shows the presence of dead ice at the end of the Qimgan Glacier. The overall velocity of the dead ice is very small, and it is difficult to visually interpret through remote sensing images. The glacial boundaries of RGI 6.0 and CGI2 both contain all portions of the debris area, but it is obvious that WCGI 2018 includes a part of the dead ice. However, Figure 13 shows that the integration of multiple remote sensing image features can accurately extract the debris area, irrespective of whether there is dead ice in the debris area.