Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

Abstract

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear; both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.