## 3 Results
### Comparison of Classification Results with Other Land Cover Products  
In order to assess the land cover results obtained in this study, the classification results obtained in this research were compared with existing high-spatial-resolution land cover products, including the FROM-GLC30, FROM-GLC10, Land Cover Dataset for the Qilian Mountains Area from 1985 to 2019 (V2.0) (LCD-QLM (V2.0)), and GlobeLand30. Figure 6 presents a visual comparison of the classification results obtained in this research and the existing land cover products in three different magnified areas. The biggest difference between the different land cover products lay in areas with complex land cover types. Therefore, three areas in the QLM with complex land cover types that included construction land, grassland, forests, farmland, and water bodies were selected as the magnified areas. We also took the standard false-color composited images of the three regions as the true value as a reference (Figure 6a-c). It can be seen that the classification effects of the land cover results obtained in this research were significantly improved. For example, in term of the identification and classification of construction lands, the zoomed-in display of the classification results obtained in this study (Figure 6d-f) were more sufficient than FROM-GLC10 (Figure 6j-l), although FROM-GLC10 had a higher spatial resolution and accuracy among many land cover products. In addition, compared with the FROM-GLC30, GlobeLand30, and LCD-QLM (V2.0) products, the classification results of this research had a higher spatial resolution and more accurate identification of the boundaries between the land cover types.  
For the proportions of the total QLM area occupied by the different land cover types, several land cover products were used to analyze the consistency of and differences between the different land cover products (Table 4). In general, the distribution of the land cover types for several of the land cover products was basically consistent, mainly for bare land and grassland, and the sum of their area accounted for about 88% of the total area of the QLM. However, specifically, the proportions of some land cover types in the study area were different. Among them, the differences in the proportions of the grassland and unused land were more obvious, and the differences in the proportions of the water bodies and construction land were relatively small. Compared with other products, the proportion of the cropland in LCD-QLM (V2.0) was significantly smaller. The proportions of the grassland and permanent snow and ice in the study area decreased with increasing spatial resolution, while the proportions of bare land increased with increasing spatial resolution.  
Figure 6: Visual comparison of magnified areas among the classification results in this study and existing high-spatial resolution land cover products. (**a–c**) the standard false-color composited images of three regions; (**d–f**) the magnified display of the classification results obtained in this study; (**g–i**) the magnified display of the FROM-GLC30; (**j–l**) the magnified display of the FROM-GLC10; (**m–o**) the magnified display of the LCD-QLM (V2.0); (**p–r**) the magnified display of the GlobeLand30.  
The same validation sample dataset was used to evaluate the accuracies of all of the land cover products to analyze whether the results obtained in this research were better than the existing high-spatial-resolution global land cover products. Table 5 compares the accuracies of the classification results obtained in this study with those of the existing land cover products. It can be seen that the overall accuracies and Kappa coefficients of the products obtained in this study were higher than those of the data products of the FROM-GLC30, FROM-GLC10, LCD-QLM (V2.0), and GlobeLand30. Therefore, the classification effect of the 10 m spatial resolution land cover maps of the QLM obtained in this study was better than those of the existing high-resolution land cover products.