## 3 Results  
### Feature Selection and Performance of Machine Learning Models in Evaporation Prediction  
The results of six machine learning models (ANN, ExtraTrees, GBDT, LightGBM, RF, and XGBoost) for estimating E under the eight feature combinations are presented in Figure 2 and Supplementary Table S5. We can find obvious differences among the different feature combinations and among the six machine learning models. The combinations of input features have a significant impact on the estimation accuracy of predicted soil evaporation. In A1 experiment, all six machine learning models have low performance (R = 0.41-0.81, RMSE = 7.74-13.21 W/m\({}^{2}\), NSE = 0.17-0.65, and MAE = 3.53-7.01 W/m\({}^{2}\)), which indicates that E is still strongly affected by other factors except for its intrinsic seasonal variation. When adding the meteorological factors (A2), the performances of all machine learning models have been clearly improved (R = 0.73-0.86, RMSE = 7.24-10.18 W/m\({}^{2}\), NSE = 0.53-0.74, and MAE = 3.68-5.36 W/m\({}^{2}\)), implying the great influence of meteorological factors on E. In the A3-A6 experiments, the performances of the six machine learning models improved slightly when the effects of H, RECO, SWC1, and vegetation index are considered separately. However, the performance varies greatly among the different machine learning models. This indicates that these feature combinations are poorly stable in E prediction. Regarding A7 that considers all factors except for meteorological factors, the six machine learning models also exhibit consistently better performances than A3-A6, with R = 0.71-0.84, RMSE = 7.36-10.22 W/m\({}^{2}\), NSE = 0.50-0.71, and MAE = 3.42-5.18 W/m\({}^{2}\). Nevertheless, the performances of the six machine learning models are observed to be highest in A8 when all factors are considered as input features (R = 0.83-0.88, RMSE = 6.87-8.08 W/m\({}^{2}\), NSE = 0.68-0.77, and MAE = 3.41-4.46 W/m\({}^{2}\)),  
Figure 2: (**a**) R, (**b**) RMSE, (**c**)NSE, and (**d**) MAE of ANN, ExtraTrees, GBDT, LightGBM, RF, and XGBoost across eight experiments. compared to other feature combinations. This can be attributed to the complex impacts of diverse abiotic and biotic factors (e.g., soil moisture content, climatic variables, as well as plant growth conditions) on soil evaporation [58; 59]. Therefore, the feature combination in the A8 experiment is considered for subsequent E prediction and T/ET estimation. For the six machine learning models, we notice that ANN generally has lower performance than other machine learning models, especially in the A1 and A3-A6 experiments that just have one or two input features. While in other experiments with multiple features (e.g., A2, A7, and A8), the performance of the ANN model is roughly comparable to other machine learning models. This suggests that the ANN model is better in dealing with multiple features for E prediction. ExtraTrees, GBDT, and LightGBM show similar performance in the A2-A8 experiments. In comparison, XGBoost and RF usually display higher accuracy than other models in all experiments. Specifically, XGBoost has the best performance in the A2, A3, A7, and A8 experiments, whilst RF outperforms XGBoost in the A1, A4, A5, and A6 experiments (Figure 2 and Table S5). The primary differences between XGBoost and RF lie in their ensemble methods and splitting strategies. The XGBoost employs a boosting ensemble, optimizing the model iteratively, whereas the RF uses a bagging ensemble, aggregating results averaged from multiple trees. When handling the complex relationship between input variables, the gradient boosting strategy and regularization features of the XGBoost model can enhance the prediction accuracy, making it particularly suitable for feature-rich datasets. By contrast, the ExtraTrees and the GBDT perform somewhat worse due to their greater randomness and more basic frameworks. The LightGBM model optimizes speed but shows slightly lower precision.