## 4 Discussion
### Importance of Each Variable in E Prediction Based on the Machine Learning Models  
Different variable combinations have been evaluated in the E prediction, and the results in Figure 2 reveal that the A8 experiment achieves the highest accuracy when all groups of variables are considered. The meteorological factors are the primary driver for E prediction on all the six machine learning models. Among them, USTAR and VPD are regarded as the primary and secondary influencing factors, contributing more than 15% based on SHAP analysis (Table 4 and Figure 9). This result indicates that soil evaporation is mostly controlled by short-term weather conditions, which is consistent with previous studies [42] revealing that VPD and wind speed were the major factors driving nighttime soil evaporation. Moreover, TIME (A1) exerts an important impact on E prediction, with its contributions exceeding 10%. On the one hand, soil evaporation has strong seasonal and annual variations, which is represent by the TIME variable. On the other hand, the strong seasonal variations of soil evaporation are also likely due to the regular crop rotation at some cropland sites, which is captured by the XGBoost model. When H, SWC1, RECO, and vegetation indices (i.e., NDVI, EVI and LAI) are considered together (A7), they have similar performance in E prediction compared to when only meteorological factors are used. This is primarily because the variations in soil evaporation are sensitive to soil conditions and vegetation cover. When meteorological factors are combined with H, RECO, SWC1, and vegetation indices individually (S5-S11), the performances of machine learning models are enhanced, and the highest performance is observed when meteorological factors and H are combined (S5). According to SHAP analysis, the contribution of H reaches 10%, far greater than other factors (Figure 9). This is likely due to the fact that H is tightly coupled with soil evaporation through the energy balance equation, and high H values are frequently correlated with low E values. Thus, the meteorological factors and H are considered with high priority when selecting the best feature combination for E prediction. Regarding vegetation indices, their combined effect (S11) is higher than its individual influences (S8-S10), suggesting that NDVI, EVI, and LAI may not be as effective as H and SWC1 in directly capturing changes in E. However, using vegetation indices together is more suitable. Figure 8: Scatter plots of predicted and observed soil evaporation using four different depths of SWC: (**a**) TIME + SWC1, (**b**) TIME + SWC2, (**c**) TIME + SWC3, (**d**) TIME + SWC4, (**e**) TIME + SWC1 + SWC2, (**f**) TIME + SWC1 + SWC2 + SWC3, and (**g**) TIME + SWC1 + SWC2 + SWC3 + SWC4. The solid black line represents the 1:1 line, and the dashed red line is the fitted line. The individual influences of SWC1 (A5) and RECO (A4) are evaluated alone, and the results show that they have a similar impact as H and vegetation indices (A3 and A6). However, when adding them to the meteorological factors (S6, S7), the influences of SWC1 and RECO are decreased. The reason for the results may be that the variations in SWC1 and RECO are mostly driven by meteorological factors, and their influences is represented by meteorological variables, H, and vegetation indices to some extent. Nevertheless, the additions of these factors can reduce the RMSE and MAE of E prediction. Additionally, differences in feature importance or contributions are found between experiments and SHAP analysis. These differences occur because the contribution calculated using the SHAP method takes into account the interactions between input variables. Overall, VPD, USTAR, TIME, and H are the most important factors affecting the performance of