## 3 Methods
### Accuracy Evaluation  
In this study, the coefficient of determination (R2), the root mean square error (RMSE), the sum of squares due to error (SSE) and Akaike information criterion (AIC) are used as the evaluation indexes of the model R2, reflecting the degree of explanation of the independent variable relative to the dependent variable in the regression prediction model and R2\(\in[-1,1]\). The larger R2 represents a better model fit and larger degree of explanation of the independent variable relative to the dependent variable. RMSE represents the deviation of the predicted value from the true value, and the larger RMSE represents the predicted value with a larger deviation from the true value. RMSE indicates the deviation of the predicted value from the true value; the larger the RMSE, the larger the deviation of the predicted value from the true value. SSE represents the sum of the squared errors \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Type** & **Variable Name** & **Description** & **Formula** \\ \hline Spectral information & B2, B3, B4, B5, B6, B7, & Sentinel-2 image band information & \\ \hline NDVI & Normalized difference vegetation index & B8–B4 \\  & REDVI & Red-edge normalized difference & B6–B5 \\  & vegetation index & B8–B8 \\  & NDWI & Normalized difference water index & B7–B8 \\  & EVI & Enhanced vegetation index & \(2.5\times\)(B8–B4) \\   Vegetation & RVI & Ratio vegetation index & B8–B4 \\   index & DVI & Difference vegetation index & B8 –B4 \\  & VDVI & Visible-band difference vegetation index & \(2.5\times\)B8–B8–B2 \\  & SAVI & Soil-adjusted vegetation index & \(1.5\times\)(B8–B4) \\  & MSAVI & Modified soil-adjusted vegetation index & \(\left[\frac{2\times\text{B8}+1-\sqrt{(2\times\text{B8}+1)^{2}-\text{K}(\text{B8 }-\text{B4})}}{2}\right]\) \\  & NBR & Normalized burn ratio & B8–B2 \\  & NDMI & Normalized difference moisture index & B8–B1 \\ \hline  & TCB & Tasseled cap brightness & \(0.0356\times\text{B1}+0.0822\times\text{B2}+0.136\times\text{B3}+0.2611\times \text{B4}+\) \\  & TCB & Tasseled cap brightness & \(0.2964\times\text{B5}+0.3338\times\text{B6}+0.3877\times\text{B7}+\) \\  & & \(0.3895\times\text{B8}+0.0949\times\text{B9}+0.0009\times\text{B10}+\) \\  & & \(0.3882\times\text{B1}+0.1366\times\text{B12}+0.475\times\text{B8A}\) \\  & & \(0.0635\times\text{B1}+0.1128\times\text{B2}+0.168\times\text{B3}+0.348\times \text{B4}+\) \\  & TCG & Tasseled cap greenness & \(0.3303\times\text{B5}+0.0852\times\text{B6}+0.3302\times\text{B7}+\) \\  & & \(0.3165\times\text{B8}+0.0467\times\text{B9}+0.0009\times\text{B10}+\) \\  & & \(0.4578\times\text{B11}+0.4064\times\text{B12}+0.3625\times\text{B8A}\) \\  & TCW & Tasseled cap wetness & \(0.0649\times\text{B1}+0.1363\times\text{B2}+0.2802\times\text{B3}+0.3072\times \text{B4}+\) \\  & & \(0.5288\times\text{B5}+0.1379\times\text{B6}+0.0001\times\text{B7}+\) \\  & & \(0.0807\times\text{B8}+0.0002\times\text{B9}+0.003\times\text{B10}+\) \\  & & \(0.4064\times\text{B11}+0.5602\times\text{B12}+0.1389\times\text{B8A}\) \\ \hline Terrain information & DEM & Extract elevation information from the DEM & \\ \multirow{2}{*}{\
\begin{tabular}{c} Terrain \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} DEM \\ Aspect \\ \end{tabular} } & Extract aspect information from the DEM & \\  & Slope & Extract slope information from the DEM & \\ \hline \hline  & B1-B12 and B8A are 13 spectral bands of Sentinel-2. B2, B3, B4, and B8 are blue, green, red, and near-infrared bands. B5, B6, and B7 are red-edge bands (red_edge1, red_edge2, and red_edge3), and B11 and B12 are short-wave infrared bands (swir1 and swir2). & \\ \hline \end{tabular}
\end{table} 
Table 3: Variable characteristics. between the predicted values and the true values; the closer SSE is to 0, the better the model selection and fitting. A smaller AIC means a better model fit.