## 4 Discussion
### Importance of Each Variable in E Prediction Based on the Machine Learning Models
Therefore, measuring the soil evaporation and plant transpiration is highly imperative for cropland T/ET estimation based on machine learning methods. Second, the generalizability of the current model has not been fully validated. To cope with this problem, we plan to adopt a hybrid modeling strategy in future studies, i.e., integrating data from sites with different characteristics to construct the model and evaluating its predictive ability of soil evaporation on sites not involved in the training. Additionally, the process of feature selection requires ongoing optimization. The soil evaporation process is also regulated by key environmental factors, such as net radiation, soil heat flux, albedo, and land surface temperature, which necessitate further refinement in subsequent research. It is noteworthy that multicollinearity issues may arise during the feature selection process. To address this, in future studies, we propose to employ recursive feature elimination (RFE) to screen for the most representative features or to use principal component analysis (PCA) for dimensionality reduction of features, thereby reducing model complexity and enhancing its robustness. The N18 method employs a quantile (75%) random forest regressor model to train and predict ecosystem WUE (\(WUE=\frac{GPP}{ET}\approx\frac{GPP}{T}\)), and it requires some periods when T is close to ET to obtain the training dataset [36]. Thus, the reasons for the overestimation of T/ET from N18 include the following. (1) The assumption that there are sufficient days when soil evaporation is negligible, and T is equal to ET. The N18 method uses an index CSWI (conservative surface wetness index) to identify the periods when T \(\approx\) ET. However, this assumption is not appropriate for all cropland sites that are generally non-water limited, especially for paddy rice in which flooding often occurs. Additionally, a strict threshold of CSWI will lead to an insufficient training dataset or make the training dataset only include periods of water stress, which all will degrade model accuracy. According to the comparison results of the four ET partitioning methods, we suspect that most of the filtered data for training RF model are unable to meet the assumption. This means the estimated T values still include soil evaporation, consequently resulting in the underestimation of E and overestimation of T/ET. (2) The uncertainty in the selection of prediction percentiles (75%) causes overestimation. [PERSON] et al. [38] indicated that the T estimates using the 90 th or 95 th prediction percentiles of the N18 methods were similar to those of the Z16 method. The uncertainty of the Z16 method is mainly from the calculation of uWUE\({}_{\text{P}}\)[30]. Similar to the N18 method, the uWUE\({}_{\text{P}}\) calculation should meet a prerequisite that there are some periods for which E is nearly zero and T is close to ET. Another uncertainty that cannot be ignored comes from the 95 th percentile used for uWUE\({}_{\text{P}}\) calculation. [PERSON] et al. [38] reported that using the 70 th percentile with the uWUE\({}_{\text{P}}\) calculation is most comparable to the 75 th percentile used by the N18 method. For the Y22 method, it is proposed based on the leaf and ecosystem WUEs, and T/ET is calculated as their ratio. The main uncertainty in the Y22 method lies in the key parameter (g\({}_{1}\)) of the stomatal conductance model. In this study, the g\({}_{1}\) values are obtained from a global map, which is generated by interpolating g\({}_{1}\) values of different vegetation types. For C4 cropland (e.g., maize), the g\({}_{1}\) parameter refers to 1.62 \(\pm\) 0.16 according to [PERSON] et al. [64]. Generally, the g\({}_{1}\) parameter is considered as a constant. However, in fact, g\({}_{1}\) reflects the coupling between plant photosynthesis and transpiration, and it varies temporally with crop species, environmental conditions, soil water content, and other factors.