Objective The Loess Plateau is an important ecological functional area in China and also a region with severe soil erosion. Soil water content and soil organic carbon are of great significance for ecological restoration. This study focused on the slope of the Qiaogou watershed in the hilly and gully region, aiming to explore the distribution and simulation of soil water content and soil organic carbon (SOC) in the area.

Method The study employed two methods, linear regression model and state-space equation, to quantify the relationship between soil water content / SOC and related environmental factors, and compared the performance of the two methods.

Result Results indicated that there was a significant correlation between soil water content, SOC, electrical conductivity, and altitude, and these variables exhibited autocorrelation in the spatial distribution. The state-space equation was found to outperform over traditional linear regression models in simulating soil water content and SOC, as it more accurately captured the spatial relationships and interaction effects between variables. The determination coefficients of the state-space model (the highest R² for soil water content was 0.904, the highest R² for SOC was 0.992) were generally higher than those of the linear regression model (the highest R² for soil water content at 0.548, the highest R² for SOC was 0.312), while the root mean square error (RMSE) was lower than that of the linear regression model, indicating that the state-space model had a higher accuracy and reliability in simulating the spatial distribution of soil water content and SOC.

Conclusion The research results provided accurate and reliable methods for studies into the distribution and simulation of soil water content and organic carbon at the slope scale of the Loess Plateau.