## 5 Conclusions  
In this study, we examined the performance of different feature combinations and six machine learning models in cropland T/ET estimation. The results show that the XGBoost model has the best performance (R = 0.88, RMSE = 6.87 W/m\({}^{2}\), NSE = 0.77, and MAE = 3.41 W/m\({}^{2}\)) when the meteorological factors, ecosystem energy flux and respiration, SWC, and vegetation indices are used as features. Moreover, the XGBoost model in cropland T/ET estimation is validated using three other ET partitioning methods (Z16, N18, and Y22) regarding to the magnitude of T and its seasonal variations, and the validation results indicate the XGBoost model is broadly consistent with them (R = 0.83, 0.91, and 0.86). In addition, the T/ET values of eight typical crops are estimated based on the four ET portioning methods. On average, the highest T/ET is found in maize with a value of 0.619 \(\pm\) 0.119, and the lowest T/ET is in paddy rice (0.505 \(\pm\) 0.055). Soybean (0.618 \(\pm\) 0.085), winter wheat (0.614 \(\pm\) 0.08), and sugar beet (0.611 \(\pm\) 0.065) yield relatively higher T/ET than winter barley (0.590 \(\pm\) 0.058), potato (0.540 \(\pm\) 0.088), and rapeseed (0.522 \(\pm\) 0.107). Compared to existing methods (e.g., Z16 and N18 both require the assumption that T \(\approx\) ET at some period, and Y22 relies on the not easily available parameter g1), machine learning-based methods have a significant advantage in that the construction process does not require any assumptions or preconditions. Moreover, the considerable differences in T/ET among different crops suggest that precise agricultural water management for each crop is crucial for the sustainability of water resources and improvements in cropland water use efficiency. In addition, due to the lack of observation data in soil evaporation and plant transpiration for training, the overestimation of cropland T/ET using machine learning models should be treated carefully. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/agronomy15030512/s1](https://www.mdpi.com/article/10.3390/agronomy15030512/s1)]([https://www.mdpi.com/article/10.3390/agronomy15030512/s1](https://www.mdpi.com/article/10.3390/agronomy15030512/s1)), Table S1: The table of variables used in this study; Table S2:The geographic location of sixteen cropland sites in this study and the years of the data; Table S3: The different feature combinations within A2 to A6; Table S4: Optimized parameters of ANN, ExtraTrees, GBDT, LightGBM, RF, and XGBoost models in each site; Table S5: (a) R, (b) RMSE, (c) NSE, and (d) MAE of different ML models with eight experiment inputs; Table S6: Cultivation information of eight typical crops (used in the calculation of T/ET in this paper) at each site; Table S7: The results of T/ET of four ET partitioning methods; Table S8: The R of XGBoost for each site when TIME and SWC1, SWC2, SWC3 and SWC4 are input respectively; Figure S1: (a) R, (b) RMSE, (c) NSE, and (d) MAE of XGBoost and RF models at each cropland site. Conceptualization, [PERSON] ([PERSON]) and S.Y. ; Data curation, [PERSON] ([PERSON]); Formal analysis, [PERSON] ([PERSON]), [PERSON], Y.B., and [PERSON] ([PERSON]); Funding acquisition, [PERSON]; Methodology, [PERSON] ([PERSON]); Supervision, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] ([PERSON]); Validation, [PERSON] and [PERSON]; Visualization, [PERSON] ([PERSON]); Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Nos. 42201407 and 42071425) and Shandong Provincial Natural Science Foundation (No. ZR2022 QD120) and in part by the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China (Grant 2023 KJ232).