## 4 Discussion
### Factors Controlling the Spatial Distribution of the Soil Properties  
The spatial distribution patterns of soil properties can be explained by environmental covariates [54]. Based on the RF model, we identified distinct important environmental variables for the three soil properties in the study area. The important variables for the SOC content were mainly related to remote sensing and human activity. Among them, PCA2 had the strongest influence on the SOC content (Figure 6). PCA2 incorporates the spectral information (e.g., soil color) closely related to the SOC, and such information can effectively reflect the changes in the SOC content. Soil color, an indicator of soil health, is mainly determined by the SOC content [55]. Therefore, PCA2 can serve as a proxy for soil color and provide key information for the SOC modeling. The inclusion of this variable can improve the prediction accuracy and interpretability of the model. In the central part of the watershed, where the terrain is flat and agricultural land predominates, frequent cultivation activities tend to accelerate the decomposition of organic matter, leading to relatively lower levels of top soil organic carbon [56]. However, in the left region of the southeastern part, which is also predominantly arable land, the proximity to the river results in the accumulation of a significant amount of organic material eroded from upstream areas, thereby exhibiting a higher soil organic carbon content [57]. The northwestern part and the right region of the southeastern part, characterized by high vegetation cover and primarily forested, demonstrate elevated soil organic carbon levels due to substantial inputs of organic matter from the vegetation [58] (Figure 5).  
We found that the STP content was predominantly controlled by land use related to human activity (Figure 6), with the STP and TDLY exhibiting consistent spatial patterns. Generally, land use type determines the input and output, as well as the cycling and transformation processes of the soil phosphorus [59]. For example, cropland is usually planted with crops that require large amounts of phosphorus fertilizer, leading to high levels of STP. In contrast, naturally vegetated areas (e.g., woodland and grassland) in the northwestern part of the study area may have low levels of STP due to phosphorus depletion and immobilization by the vegetation. A multitude of studies have shown that land use is a major factor driving the spatial variability of the soil phosphorus at local and regional scales [60; 61], in support of our finding.  
Organism covariates, particularly PET, had a prominent influence on the SAP content in the study area (Figure 6). PET describes the maximum evapotranspiration rate occurring under specific environmental conditions (e.g., temperature, humidity, solar radiation, and wind speed), when there is no limitation of water supply [62]. The phosphorus dynamics in the soil can be indirectly affected during evapotranspiration [63]. For example, strong evapotranspiration is likely to cause a decrease in the SM content, which in turn affects the dissolution and transport of phosphorus in the soil. Therefore, PET can be used as a predictor for the SAP.  
Some relevant variables, such as SM, were not adopted in the RF model after variable selection by RFE, mainly due to the relatively low resolution of these data. Furthermore, the small watershed scale and flat topography limited the contribution of other environmental covariates, such as climate and relief, to the model.