## 4 Discussion
### Importance of Each Variable in E Prediction Based on the Machine Learning Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiments** & **Input Features** & **R** & **RMSE** & **NSE** & **MAE** \\ \hline S1 & TIME + VPD & 0.820 & 7.813 & 0.671 & 3.914 \\ S2 & TIME + USTAR & 0.833 & 7.921 & 0.694 & 4.152 \\ S3 & TIME + TA & 0.813 & 7.955 & 0.659 & 3.930 \\ S4 & TIME + LW & 0.801 & 8.174 & 0.640 & 4.097 \\ S5 & TIME + VPD + USTAR + TA + LW + H & 0.870 & 7.053 & 0.757 & 3.558 \\ S6 & TIME + VPD + USTAR + TA + LW + RECO & 0.865 & 7.193 & 0.748 & 3.638 \\ S7 & TIME + VPD + USTAR + TA + LW + SWC1 & 0.868 & 7.122 & 0.753 & 3.596 \\ S8 & TIME + VPD + USTAR + TA + LW + NDVI & 0.867 & 7.140 & 0.752 & 3.610 \\ S9 & TIME + VPD + USTAR + TA + LW + EVI & 0.867 & 7.130 & 0.752 & 3.605 \\ S10 & TIME + VPD + USTAR + TA + LW + LAI & 0.866 & 7.176 & 0.749 & 3.641 \\ S11 & TIME + VPD + USTAR + TA + LW + NDVI + EVI + LAI & 0.869 & 7.091 & 0.755 & 3.559 \\ S12 & TIME + VPD + USTAR + TA + LW + H+RECO & 0.872 & 7.015 & 0.760 & 3.531 \\ S13 & TIME + VPD + USTAR + TA + LW + H+SWC1 & 0.875 & 6.950 & 0.765 & 3.493 \\ S14 & TIME + VPD + USTAR + TA + LW + H+NDVI + EVI + LAI & 0.876 & 6.916 & 0.767 & 3.455 \\ S15 & TIME + VPD + USTAR + TA + LW + H+NDVI + EVI + LAI + SWC1 & 0.878 & 6.877 & 0.770 & 3.414 \\ S16 & TIME + VPD + USTAR + TA + LW + H+NDVI + EVI + LAI + RECO & 0.875 & 6.933 & 0.766 & 3.453 \\ S17 & TIME + VPD + USTAR + TA + LW + H+NDVI + EVI + LAI + SWC1 + RECO & 0.878 & 6.874 & 0.770 & 3.408 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experiments using different combinations of input features (TIME, VPD, USTAR, TA, LW, H, RECO, SWC1, NDVI, EVI, and LAI). Figure 9: SHAP values of the model input variables in the prediction of soil evaporation. (**a**) The mean absolute SHAP value across 16 sites for each input variable, with a dot representing a flux site; (**b**) the SHAP summary plot of the input variables from all sites, with a dot representing a sample. The SHAP contribution (%) in (**a**) is calculated as the ratio of the SHAP value of each variable to the sum of all absolute SHAP values. XGBoost models in cropland T/ET estimation. [PERSON] et al. [37] demonstrated that the primary variables affecting soil evaporation were VPD, USTAR, H, and TA in flooded ecosystems. [PERSON] et al. [28] reported that the important features in predicting soil evaporation in cropland sites were USTAR, H, and VPD. The Performance and Limitations of the Machine Learning Model and Three Other Methods in ET Partitioning  
Due to the lack of observed T and E data in cropland sites, the accuracy of the four ET partitioning methods in crop T/ET estimation is verified indirectly by each method, instead of using direct validation. Therefore, obvious differences in cropland T/ET estimation results are observed among the four ET partitioning methods. Specifically, N18 and X24 tend to overestimate crop T/ET, while Z16 and Y22 have a tendency to underestimate those values. The reasons for the differences are various. For the X24 method, it performs well at many cropland sites (Figure 4), such as DE-Rus, US-Twt, and US-Tw3 (R \(>\) 0.9 and NSE \(>\) 0.8). However, its predicted T values are unexpectedly high. A major reason for this is that the XGBoost model obviously underestimates soil evaporation in its high-value range (E \(>\) 100 W/m\({}^{2}\), Figures 3 and 4). Considering that high soil evaporation is more common in the daytime than in the nighttime for croplands, there is no doubt that the cropland T/ET is overestimated. Another possible reason for the relatively high T/ET values is that the influencing factors of soil evaporation are not completely considered, and the magnitude of their influence differs between daytime and nighttime. This causes large errors in daytime soil evaporation estimation for the XGBoost model, which is entirely trained by the nighttime data. There are some limitations of X24 in this study. First, due to the lack of in situ measurements in these 16 cropland sites, our X24 method has not been validated by direct evaporation measurement data.