## 3 Results and Discussion
### Contribution of Influencing Factors to the Predicted Daily \(\rm ET_{a}\) of Tea Plantation  
The contributions of the influencing factors on the predicted daily \(\rm ET_{a}\) from the studied models were assessed using the Shapley value. Figure 9 displays the key statistical parameters for the six machine learning models by using the whole dataset (Scenario 1) in the validation phase. All models consistently detected \(\rm R_{n}\) as the most vital factor. Meanwhile, a positive correlation relationship between daily \(\rm ET_{a}\) and \(\rm R_{n}\) was also found. In addition, \(\rm T_{mean}\) also made a high contribution to tea plantation \(\rm ET_{a}\) estimation for all machine learning models. By contrast, the contributions of other features varied in the different models. In the KNN and SVM models, \(\rm W_{s}\) was the third important feature, and the effects of the remaining features were considerably smaller. For MLP and RF models, LAI was the third important feature. Meanwhile, RH was the third important feature for the Ad and bagging models. However, the importance of LAI and RH also decreased significantly. The contribution of \(\rm S_{m}\) was considerably less of a feature for all models in our study. The results suggested that energy conditions (\(\rm R_{n}\) and \(\rm T_{mean}\)) are the key drivers for tea plantation \(\rm ET_{a}\) estimation. This finding agreed with those of previous studies, which reported that solar radiation and temperature are the most important climatic variables that influence \(\rm ET_{a}\) variation in different ecosystems in humid regions [6; 68]. Meanwhile, this finding was further supported by observation studies that used eddy covariance, which implied that \(\rm R_{n}\) acted as a primary driver of evapotranspiration in tea plantations and determined the temporal variation in \(\rm ET_{a}\) by more than 70% for tea plantations in our study area [5; 6]. These results also suggest that the models that used machine learning methods in our study possess a certain physical mechanism for the \(\rm ET_{a}\) estimation of tea plantations.  
Apart from the strong effect of energy conditions on tea plantation \(\rm ET_{a}\) estimation, intense extreme climatic events and human-induced activities, such as canopy pruning, weeding, drainage, and extreme drought, will alter the water and heat exchange process, thereby increasing the complexity of the evaluation process of tea plantation \(\rm ET_{a}\). Significant underestimation of \(\rm ET_{a}\) by our machine learning models was noted with extreme climatic events and human-induced events in the current study. Hence, the contributions of global warming and human activities to the change in \(\rm ET_{a}\) should be noted in the future and the prediction accuracy of evapotranspiration using machine learning methods should be improved to provide better decision making for the sustainable water resources and agricultural irrigation system planning of tea plantations.  
Figure 6: Percentage increase or decrease in validation RMSE over testing RMSE for six machine learning models.  
Figure 7: RMSE value variations in different sub-intervals.  
Figure 8: RMSE value variations in different seasons (E: early-growing season; M: middle-growing season; L: late-growing season; N: non-growing season).