## 5 Conclusions  
In this study, we predicted the spatial distribution patterns of the soil organic carbon, total phosphorus, and available phosphorus contents in the Haihun River sub-watershed. To improve the prediction performance, we incorporated variable selection into two machine learning models. Recursive feature elimination was used to eliminate any redundant variables and to develop the optimal prediction models for multiple soil properties. The machine learning models with variable selection showed a notably improved performance compared to the all-variable models.  
The spatial distribution of the soil properties exhibited consistent overall trends based on the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection. This consistency indicates the effectiveness and accuracy of the machine learning models used in this study. There was no distinct difference in the prediction accuracy of the soil properties between the two machine learning models. Nevertheless, the RF model presented a relatively low prediction uncertainty and a more robust predictive ability.  
Among the variables included in the model predictions, remote sensing covariates (especially principal component 2) predominantly controlled the distribution of the soil organic carbon. Human activity covariates (mainly land use) played a major role in governing the distribution of soil total phosphorus. Organism covariates (represented by potential evapotranspiration) were the most important factors affecting the distribution of the soil available phosphorus. The results can provide guidance on soil management in the study area and serve as a methodological reference for the prediction of soil properties in other similar sub-watersheds.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s24123784/s1](https://www.mdpi.com/article/10.3390/s24123784/s1)]([https://www.mdpi.com/article/10.3390/s24123784/s1](https://www.mdpi.com/article/10.3390/s24123784/s1)), Table S1: Multicollinearity diagnostic of SOC; Table S2: Multicollinearity diagnostic of STP; Table S3: Multicollinearity diagnostic of SAP.  
**Author Contributions:** Conceptualization, [PERSON] and Y.J. ([PERSON]); methodology, [PERSON] and J.L.; software, [PERSON] and J.L.; validation, [PERSON] ([PERSON]), Y.Y., and Y.J. ([PERSON]); formal analysis, Y.Y.; investigation, Y.L.; resources, X.Q.; data curation, L.Z.; writing--original draft preparation, J.H. and J.L.; writing--review and editing, [PERSON] and J.L.; visualization, J.L.; supervision, [PERSON] ([PERSON]); project administration, [PERSON] and X.Q.; and funding acquisition, J.H. and L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Jiangxi Geological Bureau Young Science and Technology Leader Training Programme Project (2022)XDZKJRC08) and the National Key Research and Development Program of China (2022 YFD1900601-4).  
**Data Availability Statement:** The data are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.