## 5 Conclusions  
Based on the Google Earth Engine and the various remote sensing satellite data provided by it, a feature space composed of feature variables such as spectral bands, spectral indices, terrain features, texture features, and radar features was constructed. Three remote sensing image classification algorithms, including the SVM, CART, and RF algorithms, were used to automatically extract the land cover types in the Qilian Mountains. The accuracies of the classification results were evaluated, and the impacts of the different classification algorithms and the different feature variable combinations on the classification results were analyzed. In addition, the comparison between the classification results obtained in this research and existing high-spatial-resolution products were conducted. The results showed that the GEE platform, based on the cloud framework, had the advantages of convenient data acquisition and strong data-processing capabilities, and could be used to quickly and accurately extract and map land cover types in a large area. Moreover, the three remote sensing image classification algorithms assessed in this study could be used to obtain classification results with high classification accuracies. The classification results obtained using the three classification algorithms had high consistency in terms of the area ratio and the distribution characteristics of the land cover types, but there were still differences in the classification accuracies of the classification results. Among them, in terms of the OA of the classification results, the RF classifier was the highest, with an average OA of 96.51%, followed by the SVM classifier (94.67%) and CART classifier (94.50%). Therefore, the most appropriate classification algorithm was RF for the land cover classification in QLM. Furthermore, the participation of feature variables in the classification process improved the OA of the land cover classification results. Specifically, the participations of spectral indices and radar features significantly improved the OA of the SVM classifier, with values of 0.76% and 1.33%, respectively; the participations of spectral indices significantly improved the OA of the CART classifier, with a value of 1.65%, and the participations of each type of feature variables increased the OA of the RF classifier, with an average value of 0.33%. Nevertheless, there were still cases in which the different feature variables had different effects on the different classifiers, and the contributions of the different feature variables to the identification and extraction of the different land cover types were also different. These phenomena were reflected in the UA and PA values of different land cover types in the classification results when different feature variables combinations participated in the classification. Finally, compared with the existing global land cover products, the 10 m spatial resolution land cover map of the QLM obtained in this research had a higher spatial resolution and accuracy. When the same validation sample dataset was used to evaluate the accuracies of all land cover products, the OA of the classification results of this research was higher than the OA of FROM-CLC10, with a value of 7.51%. This gap was much higher compared to other land cover products. The results of this study provided a scientific basis for other related studies in the QLM, as well as a reference for high-spatial-resolution land cover classification and mapping of large areas. Conceptualization, X.W. and D.Y. ; methodology, X.W. and Y.Y. ; software, X.W. and Y.Y. ; validation, Y.Y. ; resources, X.W. ; writing original draft preparation, Y.Y. ; writing--review and editing, Y.Y., X.W., D.Y., Z.Z. and Z.N. ; funding acquisition, X.W. and D.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Science and Technology Major Project of China's High Resolution Earth Observation System (Project No.