## 6 Conclusions  
This research uses Landsat and DEM as data sources, comparing and improving upon different classifier construction strategies (RF, CNN, and RF-CNN) to obtain multiple single-classifier outputs with certain differences, and then performing decision-level fusion of these outputs to obtain the final debris-covered glacier classification result. In general, the RF method performs best overall, while the RF-CNN method can provide a promising reference value for the extraction of debris-covered glaciers. This result helps to select  
Figure 16: Analysis of the correlation of variables in the RF-CNN model for the classification of debris-covered glaciers. * (\(p\leq 0.05\)) denotes a significant correlation.  
the optimum classifier and improves the overall classification accuracy, striking a better balance between the classification effects of different methods.  
The development of a machine learning method (RF-CNN) can effectively suppress classification uncertainty and further improve the generalization performance of the classifier is important. Combining various classification models also helped to mitigate the challenges regarding detection of the active and inactive debris-covered glaciers. Further, we analyzed the advantages and disadvantages of composite classification models in debris-covered glacier classification, and found that the performance of the machine learning model and the results of the final debris-covered glacier extraction are strongly dependent on the intensity of the debris coverage: the higher the proportion of surface debris coverage in the area, the more accurate the result, and vice versa. This can help to improve the glacier classification method and create conditions for rapid glacier inventory.  
In subsequent research on glacier classification, more samples should be extracted to optimize the RF-CNN model. Based on the statistical characteristics of each pixel level of remotely sensed SAR images, this method of fine-tuning the network trained with optical images is not suitable for SAR image classification. In future work, the RF-CNN network model should be further improved to facilitate glacier classification while considering SAR images.  
Conceptualization, Z.Z. and Y.L.; Methodology, Y.L.; Validation, Y.L.; Resources, Y.L. and D.S.; Writing--original draft preparation, Y.L. and Z.Z.; Writing--review and editing, Y.L. and Z.Z.; Visualization, Y.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42071085), Open Project of the State Key Laboratory of Cryospheric Science (Grant No. SKLCS 2020-10), and the National Nature Science Foundation of China (Grant No.41701087).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The Landsat images can be freely downloaded from [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), (accessed on 2 July 2021). We thank the USGS for free access to Landsat data. Meanwhile, we would like to thank 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 the ASTER GDEM V2 data. Thanks to the Institute of Tibetan Plateau Research, Chinese Academy of Sciences for providing the second glacier inventory dataset of China. 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 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).  
The authors declare no conflict of interest.