## 2 Materials and Methods
### Methods
#### 2.3.2 Feature Construct  
* Spectral indices  
Existing studies have shown that the application of remote sensing spectral indices can effectively improve the accuracy of the identification of different land cover types. In this study, the normalized difference vegetation index (NDVI) [40], the enhanced vegetation index (EVI) [41], the land surface water index (LSWI) [42], the normalized difference water index (NDWI) [43], the soil adjusted vegetation index (SAVI) [44], and the bare soil index (BSI) [45] were calculated using the GEE cloud platform based on the formulas for each spectral index, and each index was added to the original remote sensing image in turn (as shown in Figure 2). The calculation formulas for each index are as follows:  
\[\text{NDVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{RED}}}{\rho_{\text{NIR}}+\rho _{\text{RED}}} \tag{6}\]  
\[\text{EVI}=2.5\times\frac{\rho_{\text{NIR}}-\rho_{\text{RED}}}{\rho_{\text{ NIR}}+6\times\rho_{\text{RED}}-7.5\times\rho_{\text{BLUE}}+1} \tag{7}\]  
\[\text{LSWI}=\frac{\rho_{\text{NIR}}-\rho_{\text{SWIR}}}{\rho_{\text{NIR}}+ \rho_{\text{SWIR}}} \tag{8}\]  
\[\text{NDWI}=\frac{\rho_{\text{GREEN}}-\rho_{\text{NIR}}}{\rho_{\text{GREEN }}+\rho_{\text{NIR}}} \tag{9}\]  
\[\text{SAVI}=\frac{(\rho_{\text{NIR}}-\rho_{\text{RED}})(1+\text{L})}{(\rho_ {\text{NIR}}+\rho_{\text{RED}}+\text{L})} \tag{10}\]\[\text{BSI}=\frac{(\rho_{\text{RED}}+\rho_{\text{SWIR}})-(\rho_{\text{NIR}}+\rho_{ \text{BLUE}})}{(\rho_{\text{RED}}+\rho_{\text{SWIR}})+(\rho_{\text{NIR}}+\rho_{ \text{BLUE}})} \tag{11}\]  
where \(\rho_{\text{SWIR}}\), \(\rho_{\text{NIR}}\), \(\rho_{\text{RED}}\), \(\rho_{\text{CREEN}}\), and \(\rho_{\text{BLUE}}\) are the surface reflectance values of the shortwave infrared, near-infrared, red, green, and blue bands, respectively. L is the soil regulation factor, which ranges from 0 to 1 and is usually assigned a value of 0.5 to better reduce the background difference of the soil and eliminate the noise impact of the soil [44]. * Texture features The texture features are an important attribute of remote sensing images, and different land cover types have different texture features. Based on the texture features, the accuracy of the recognition and classification can be improved [46]. The Gray-Level Co-occurrence Matrix (GLCM) is a classic method to extract the texture features [47]. It extracts textures through the conditional probability density between gray levels of remote sensing images, and is diffusely used in land cover classification research. The calculation of the GLCM can be obtained through the \"glcmTexture\" function in the GEE cloud platform. In this study, several commonly used texture features were selected for use in the classification, including the feature parameters of angular second moment (asm), contrast (con), correlation (corr), variance (var), inverse different moment (idm), sum average (savg), and entropy (ent) based on NDVI. The calculation of each feature variable was based on GLCM, they were dimensionless, and their range of values was not completely uniform. * Radar features Studies have shown that SAR data are sensitive to land cover types such as water bodies, construction lands, and croplands [23]. The radar variables involved in the construction of the feature variables in this study included the backscatter coefficients of the Sentinel-1 data in VV polarization and VH polarization modes. * Terrain features In the recognition and classification of remote sensing images, the participation of terrain features can improve the accuracy of the classification. Therefore, based on the SRTMGL1_003 digital elevation data product, the \"ee.Terrain.products\" function provided by GEE was used to calculate the aspect, hill shade, slope, and elevation, and they were added to the remote sensing images as independent features. According to the feature variables described above, the classifications of image were conducted in five feature variable combinations.