## 4 Results
The validation sample was selected based on the Landsat 8 images listed in Table 1. Pixel-based classification assessment indicators include the overall accuracy, Kappa coefficient, user's accuracy, and producer's accuracy, based on the classification confusion matrix [77]. We calculate the accuracy of classification by comparing the position of each test pixel with its corresponding position in the classification image, the results of which are given in Table 5. In general, the RF method performs best overall, not the RF-CNN method. Both its Kappa coefficient and user/producer's accuracy is higher. However, the RF-CNN method can provide a plausible reference value for the extraction of debris-covered glaciers. The Eastern Pamir and Nyaingentanglha regions are obviously different, and we pay special attention to the relational mapping between the intensity of the debris coverage and the construction of the machine learning model to compare the size of the debris coverage and the quality of the model training in different areas. The results of model training differ slightly from sample to sample. In Eastern Pamir, the total area occupied by the glaciers was 605.117 km\({}^{2}\), of which 85.398 km\({}^{2}\) was covered in debris and 519.718 km\({}^{2}\) was not. Debris-covered glaciers in Nyaingentanglha were mainly distributed between 4000 and 6000 m, with an area of approximately 126.736 km\({}^{2}\). The possible reason for this is that the sample sizes in Eastern Pamir and Nyaingentanglha are different. Eastern Pamir was involved in the training and testing of 7499 samples, while Nyaingentanglha was involved in the training and testing of only 3099 samples. The fact is that the debris coverage rate in Nyaingentanglha is indeed not as high as that in Eastern Pamir. It is clear that the size of the sample directly determines the result (Figure 12). In areas with more surface debris, sufficient labels and sample data are provided, and the trained model performs better. From the surface debris data, Eastern Pamir provided more training data than Nyaingentanglha in this experiment, so the results for the former are more accurate. Furthermore, the cloud coverage, snow coverage, and terrain shadow coverage of the glacier surface are all specific indicators that affect the classification results. These factors are mainly determined by the quality of the image, which is determined by the time taken by the sensor to acquire the image, illumination conditions, and climatic conditions. We have mitigated the influence of cloud coverage when selecting remote sensing images (the cloud coverage is less than 10%), primarily to eliminate the clouds over the glacier and obtain its true spectral properties. Further, our study is focused on summer, when the snow coverage is the lowest in the year. Terrain shadow coverage is mainly determined by the sun azimuth and sun altitude. Particularly for mountain glaciers with undulating terrain, such as Eastern Pamir and Nyaingentanglha, the phenomenon of shadows obscuring the glacier can only be reduced, not eliminated. Future research should focus on the impact of these shadow areas on automatic glacier extraction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Eastern Pamir** & **RF** & **CNN** & **RF-CNN** \\ \hline Overall Accuracy & 97.60\% & 96.34\% & 98.14\% \\ Kappa Coefficient & 0.96 & 0.95 & 0.97 \\ User’s Accuracy & 91.59\% & 87.96\% & 97.90\% \\ Producer’s Accuracy & 97.17\% & 98.69\% & 98.33\% \\ \hline
**Nyaingentanglha** & & & \\ \hline Overall Accuracy & 99.31\% & 99.06\% & 97.62\% \\ Kappa Coefficient & 0.98 & 0.97 & 0.94 \\ User’s Accuracy & 92.53\% & 78.75\% & 90.60\% \\ Producer’s Accuracy & 98.86\% & 97.53\% & 74.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy assessment. Figure 12: Comparison of experimental performance indicators extracted for Eastern Pamir and Nyainotanglha debris-covered glaciers based on the RF-CNN model (P: Eastern Pamir and N: Nyainotanglha).