## 3 Results and Discussion
### Analysis of Factors Affecting the Model's Estimation of Soil Temperature
To quantify the effects of soil water content, soil organic matter content, and other meteorological factors on T\({}_{\rm s}\) estimation by models in different climatic zones, the relationships between these factors and the RMSE values of XGBoost1 were analyzed (Figure 6). The spatial distribution of soil organic matter content and soil water content for the selected stations are plotted in Figure 7. Data from different climatic zones were fitted separately. For each of the soil texture factors or the meteorological factors, its correlation with the RMSE of T\({}_{\rm s}\) differed between climatic zones, suggesting that the model accuracy in estimating T\({}_{\rm s}\) was related to the soil texture and the climate of the region where the meteorological stations were located. Different soil textures have different permeability and nutrient contents, which partially leads to differences of T\({}_{\rm s}\) variability and consequently affects the model accuracy for T\({}_{\rm s}\) estimation. This pattern is consistent with results described in the previous section. As shown in Figure 6a, the most significant correlation between soil organic matter content and model estimating accuracy was found in the NC region (R\({}^{2}\) = 0.255), followed in order by the IM, the NWC, and the CC regions. In other regions, including NEC, SC, and QTP, no significant correlation was found. In regions where the correlation was significant, the model accuracy for estimating T\({}_{\rm s}\) decreased with the increasing soil organic matter content. This is due to the fact that the specific heat capacity of soil organic matter is less than the soil specific heat capacity. Under certain conditions, the higher the organic matter content is, the more heat absorption should be required for decomposition, leading to more variable and unstable T\({}_{\rm s}\). [PERSON] et al. (2018) also concluded that soil organic matter could significantly affect the variation of T\({}_{\rm s}\). According to the results, the NEC, SC, and QTP regions had relatively high soil organic matter contents, indicating that too much soil organic matter may lead to other types of reactions within the soil and make the model estimation of T\({}_{\rm s}\) less stable ([PERSON] et al., 2018; [PERSON] et al., 2018). Figure 6: Fitting relationships between several factors ((**(a)**) soil organic matter; (**(b)**) soil water content; (**(c)**) RH; (**(d)**) R\({}_{s}\); (**(e)**) U\({}_{2}\)) and model performance in estimating T\({}_{\rm s}\) (* indicates significant correlation. **(**)** indicates highly significant correlation.). The correlation between soil water content and model estimating accuracy was most significant in the IM region (R\({}^{2}\) = 0.175; Figure 6b), followed by the CC region, and then the QTP and the NC regions. Soil water content was not significantly correlated with the accuracy of estimating T\({}_{\rm s}\) in the NWC, NEC, and SC regions. In most regions where the correlation was significant, the model estimating accuracy increased with the increasing soil water content. This might be attributed to the fact that the specific heat capacity of water is about four times higher than that of soil. The temperature change of the soil water is much smaller than the soil itself when equally heated or cooled, suggesting that soil water  
Figure 7: Spatial distribution of soil organic matter content (**a**) and soil water content (**b**). can indirectly regulate \(T_{s}\) and reduce the range of \(T_{s}\) variation. [PERSON] [80] also concluded that soil water content had an important influence on soil thermal properties and could effectively regulate \(T_{s}\). Therefore, under certain conditions, the higher the soil water content is, the smaller is the expected range of \(T_{s}\) variation, leading to higher model accuracy in \(T_{s}\) estimation. This finding is consistent with conclusions from previous studies [81; 82].