## 4 Conclusions  
Soil temperature (T\({}_{\rm s}\)) has an important influence on the energy conversion between the atmosphere and the soil. In this study, three data-driven algorithms (i.e., M5P, RF, and XGBoost) were selected to model T\({}_{\rm s}\) at a 0 cm depth using different combinations of meteorological factors (including T\({}_{\rm max}\), T\({}_{\rm min}\), T\({}_{\rm mean}\), RH, R\({}_{\rm s}\), and U\({}_{2}\)) as inputs. The three types of tree-based models were evaluated and compared at 689 meteorological stations in seven different climate zones of China. The results showed that the XGBoost model performed better and was more computationally efficient for estimating T\({}_{\rm s}\) compared to the M5P and RF models in different climate zones. With the inputs of T\({}_{\rm mean}\) and R\({}_{\rm s}\), the models exhibited similar performance to that of models with the complete combination of meteorological factors as inputs, both of which had good estimation performance, but the former required fewer meteorological factors and datasets. On the basis of T\({}_{\rm mean}\)  
Figure 10: Frequency distribution of absolute errors (AE) between measured and estimated daily T\({}_{\rm s}\) obtained by three machine learning models under various input combinations (**a–f**) during testing. adding \(\rm R_{s}\) to the model input had more significant influence and importance on model estimating accuracy than adding other climatic factors such as RH and \(\rm U_{2}\) to the input. There was diversity in the estimating performance of the models across climate zones for the same combination of inputs. Comparing different climatic zones, the lower variability of temperature and radiation in the CC and SC regions resulted in a better model estimating accuracy, while the larger variability of radiation in the IM and NEC regions led to poorer model performance for estimating \(\rm T_{s}\). Soil organic matter content, soil water content, \(\rm T_{mean}\), RH, \(\rm R_{s}\), and \(\rm U_{2}\) were significantly related to model accuracy for estimating \(\rm T_{s}\), suggesting that these factors all contributed to model errors during the estimation of \(\rm T_{s}\). Overall, for estimating \(\rm T_{s}\) at the 0 cm depth, the XGBoost model is highly recommended, and if the study area covers different climatic zones of China, the XGBoost model with the combination of \(\rm T_{mean}\) and \(\rm R_{s}\) as inputs would obtain satisfactory results for \(\rm T_{s}\) estimation across different regions. However, it is to be noted that there is variability in \(\rm T_{s}\) at different soil depths, and other meteorological factors (e.g., precipitation) would also impact \(\rm T_{s}\). Therefore, for further studies of \(\rm T_{s}\) estimation, the use of different soil depths as well as additional meteorological and environmental factors should be considered. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12105088/sl](https://www.mdpi.com/article/10.3390/app12105088/sl)]([https://www.mdpi.com/article/10.3390/app12105088/sl](https://www.mdpi.com/article/10.3390/app12105088/sl)). Figure S1. Performance of data-driven models (M5P2: a, b; RF2: c, d; XGBoost2: e, f) for the estimation of \(\rm T_{s}\) with \(\rm T_{mean}\) as inputs. Figure S2. Performance of data-driven models (M5P3: a, b; RF3: c, d; XGBoost3: e, f) for the estimation of \(\rm T_{s}\) with \(\rm T_{mean}\) and RH as inputs. Figure S3. Performance of data-driven models (M5P4: a, b; RF4: c, d; XGBoost4: e, f) for the estimation of \(\rm T_{s}\) with \(\rm T_{mean}\) and \(\rm R_{s}\) as inputs. Figure S4. Performance of data-driven models (M5P5: a, b; RF5: c, d; XGBoost5: e, f) for the estimation of \(\rm T_{s}\) with \(\rm T_{max}\) and \(\rm T_{min}\) as inputs. Figure S5. Performance of data-driven models (M5P6: a, b; RF6: c, d; XGBoost6: e, f) for the estimation of \(\rm T_{s}\) with \(\rm T_{mean}\) and \(\rm U_{2}\) as inputs. Figure S6.