## 4 Discussion
### Influence of Feature Variables on Remote Sensing Classification  
The GEE cloud platform can be used to evaluate the contribution of the feature variables participating in the RF classifier. In this study, the average importance scores from high to low were as follows: terrain features, radar features, spectral bands, spectral indices, and texture features. This result was inconsistent with the results of several existing studies. For example, [PERSON] et al. used the RF classifier and fused Landsat data and Sentinel data to classify and map Africa. Their results indicated that the highest importance scores for the feature variables participating in the classification were those of the NDVI_S2 and NDVI_L8, and the lowest importance scores were for Band2 and Band8A of the Sentinel-2 and Band2 and Band10 of Landsat 8 [52]. [PERSON] et al. used Landsat 8 OLI data and a GEE-based RF classifier to map the urban areas of three cities in China. In the importance analysis of the variables, NDVI and NDWI had the highest importance scores [59]. These results were also consistent with the findings of previous reported studies. [PERSON] et al. used the RF classifier to classify the Landsat TM and OLI data for the Gannan Prefecture from 2000 to 2018. Their results showed that the topographic features, including the altitude and slope, contributed the most, followed by the spectral indices and spectral bands [53]. [PERSON] et al. used Landsat 8 data from different seasons and the RF classifier based on GEE to classify the land cover in Mongolia. The results showed that among all the input variables, elevation was the most important variable, followed by other feature variables [60]. Therefore, if the data sources used for the classification and the study areas were different, the importance scores of the feature variables input into the RF in the different studies were also different. When different classification algorithms were used for classification, the participations of different feature variables had different impacts on the PA values of the different land cover types (Table 2 (Appendix A)). When three classification algorithms were used in an image classification, compared with the land cover types of the croplands, shrublands, wetlands, and construction lands, other land cover types had high PAs and were less affected by the feature variable combinations. In addition, compared with CART and RF, the differences in the feature variables had a greater impact on the PA of the various land cover types when the SVM classification algorithm was used. Specifically, the participation of the most feature variables in the classification increased the PA values of the land cover types, including croplands, shrublands, wetlands, and construction lands. It is worth noting that the participation of spectral indices greatly increased the PA of croplands, by 24.91%; and the participations of terrain features and radar features greatly increased the PA of construction lands, by 45.02% and 41.09%, respectively. In contrast, there were also cases in which the feature variables had negative impacts on the PAs of some of the land cover types. Among them, the participation of texture features caused decreases in the PAs of the croplands and construction lands, by 3.27% and 1.66%, respectively. For the CART classification algorithm, in general, the participation of most of the feature variables had a positive effect on the increase in the PAs of most of the land cover types. Among them, the participation of the terrain features significantly increased the PAs of wetlands and construction lands, by 17.91% and 13.64%, respectively; and the participation of the radar features significantly increased the PA of construction lands, by 14.24%. However, when the radar features were inputted, the PAs of the croplands, shrublands, and wetlands decreased slightly (by 5% on an average).