## 3 Results and Discussion
### Analysis of Factors Affecting the Model's Estimation of Soil Temperature  
To compare the level of linearity between the estimated and the measured values of T\({}_{\rm s}\) for each model, we randomly selected one meteorological station per region (including stations 51,477, 50,618, 50,353, 52,986, 56,188, 59,287, and 52,984, respectively) out of the 689 stations in the seven different climatic zones across the country as examples for description. Outputs from models with input combinations 1, 5, and 6 were plotted for comparison (Figure 5, Figures S6 and S7). It is clear from Figure 5, Figures S6 and S7 that the three types of models (i.e., the MSP, RF, and XGBoost models) all performed very well in estimating T\({}_{\rm s}\). For any of the models constructed with the selected input combinations in the randomly selected stations, there was a significant positive correlation between the estimated and the measured T\({}_{\rm s}\) values, with R\({}^{2}\) values at least higher than 0.92. For models with the complete combination of climatic variables (i.e., combination 1) as inputs, XGBoost outperformed both MSP and RF at each of the randomly selected stations (Figure 5). Across the stations, the average R\({}^{2}\) values of XGBoost models were 0.981, higher than RF (averaged R\({}^{2}\) = 0.978) and MSP (averaged R\({}^{2}\) = 0.966) models. For each type of model, the estimating accuracy differed among stations, suggesting that variations in geographical and environmental factors among different climatic zones could affect the model performance in T\({}_{\rm s}\) estimation to some extent, as suggested by [PERSON] et al. [68]. Taking XGBoost as an example, except for station 59,287 (R\({}^{2}\) = 0.966), the model accuracy was considerably high at all stations (all R\({}^{2}\) values no less than 0.980; averaged R\({}^{2}\) = 0.983), with the highest accuracy observed at station 51,477 (R\({}^{2}\) = 0.989). The significant difference in model estimating accuracy between station 51,477 and station 59,287 could be attributed to the differences in climatic factors (e.g., temperature) between climatic zones. Station 51,477 is located in the NWC region, with sufficient light, low soil water contents, and low variation of T\({}_{\rm s}\). In contrast, station 59,287 is within the SC region, where the soil moisture is sufficient and the variation in T\({}_{\rm s}\) is high. First of all, we selected stations that are all weather stations. The terrain of the station measurement points is short grass and flat, so the slope and some human factors are not considered to influence T\({}_{\rm s}\). However, soil characteristics differ between climatic zones. For example, the soil in the CC area is mainly red loam. The main characteristic of red loam soil is strong clay and little organic matter. Therefore, the soil heat transfer is faster, which means that T\({}_{\rm s}\) does not change much. Thus, the model estimated T\({}_{\rm s}\) with high accuracy. However, the soil in the NEC area is mainly black soil. It is characterized by fertile land and high organic matter content. However, as we know, high organic matter content means higher heat requirements for microbial decomposition. Therefore, the variation in T\({}_{\rm s}\) varied widely, leading to a decrease in the accuracy of model estimation. [PERSON] et al. [69] concluded that the variation of T\({}_{\rm s}\) in different climatic zones was influenced by a combination of air temperature and precipitation. More precipitation occurs in humid areas and less in arid regions. These findings are consistent with the conclusion made by [PERSON] et al. [70], where they suggested that T\({}_{\rm s}\) could be directly affected by air temperature and indirectly impacted by soil moisture. In addition, other factors such as soil depth, soil characteristics, irrigation levels, and precipitation could also affect the estimation of T\({}_{\rm s}\) to some extent, as described in the literature [9; 71; 72; 73; 71].