## 4 Discussion  
Impacts of Crop Rotation and SWC from Different Layers on the Performance of Machine Learning Models in E Prediction  
A distinctive characteristic of the cropland sites used in this study is the diverse crop rotation. Crop rotation information reflects the area covered by the crop and thus affects the prediction of soil evaporation [61]. For the cropland T/ET estimation based on the XGBost model, we did not apply the crop rotation information as a feature in the machine learning models. This is because the addition of the crop rotation information into features has limited improvement on the accuracy of E prediction, as indicated in Table 3. The main reason for this phenomenon is that the machine learning model is trained at each cropland site, where soil properties and environmental conditions at each site remain relatively stable  
Figure 7: The multi-year mean T/ET for different crops based on four ET partitioning methods (Z16, N18, Y22, and X24). Error bars represent \(\pm\) 1 standard error. over time. This stability leads to the possibility that the effect of crop rotation information may be encompassed by seasonal variations (i.e., TIME). However, when mixing multiple cropland sites to train a unified machine learning model for T/ET estimation, the crop rotation information is useful [28]. Considering the complexity of climatic factors, crop rotation, and agricultural management practices, it is recommended to increase the number of cultivated sites to enhance representativeness and expand the dataset. Soil moisture is influenced by many factors, such as soil texture, depth, and the water table, all of which directly affect soil evaporation [58; 62; 63]. In this study, only the SWC from the top soil layer (SWC1) is used in the machine learning models, considering that the coarse spatial resolution of the ERA5-Land dataset may bring additional uncertainty in E prediction. Here, the influences of SWC from different layers (SWC1 (0-7 cm), SWC2 (7-28 cm), SWC3 (28-100 cm), and SWC4 (100-289 cm)) in E prediction are evaluated. From the results shown in Figure 8 and Supplementary Table S8, SWC1 has the largest influence on E prediction, with R = 0.805, RMSE = 8.125 W/m2, NSE = 0.645, and MAE = 4.103 W/m2. The use of SWC2, SWC3, and SWC4 alone as input feature leads to a decrease in the accuracy of E prediction (Figure 8a-d). The reason for this is that soil evaporation is mostly contributed by the top soil. As the soil depth increases, the influence of SWC on soil evaporation gradually weakens in the deeper layers. This result is in agreement with a previous study by [PERSON] et al. [55], which demonstrated that the SWC at the shallowest depth (about 0 cm) had a more significant impact on evapotranspiration compared to the SWC at 20 cm depth in alpine meadows, steppes, and deserts. The top (or shallowest) soil layer is extremely susceptible to evaporating due to its exposure to solar radiation, high temperatures, and high VPD conditions [58; 62]. Bare soil has a higher soil evaporation intensity compared to vegetated surfaces. Additionally, the addition of SWC2, SWC3, and SWC4 into SWC1 can result in better predictions (Figure 8e-g). A possible reason for this is that soil evaporation does not totally occur from the top soil layer (0-7 cm). Deep soil, especially SWC2 at 7-28 cm, also has an influence on soil evaporation. Besides, soil water can move upward from deep soil to the topmost layer, increasing the effect of deep soil on SWC1. Therefore, here, we suggest that all the four layers of SWC can be tested and used in machine learning models for E prediction. However, if only one layer of SWC can be measured at the field scale or obtained from remote sensing data, the SWC from topmost layer is most important and required.