Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model

The spatial distribution of soil organic carbon density (SOCD) is crucial for understanding land use impact on carbon budget. The spatial estimation and accurate mapping of SOCD in plains remain challenging, partly due to the relatively invariant topography and the lack of consideration of landscape patterns. Here, we propose a novel landscape metric-based regression Kriging (LMRK) for the spatial estimation of SOCD in plains. Using 242 topsoil samples collected in the Jianghan Plain, China, we (i) investigate the scale-dependent relationship between SOCD and 24 landscape metrics and (ii) develop LMRK models with multi-scale buffers (100–1000 m) for SOCD estimation and compare their performance with ordinary Kriging (OK) and regression Kriging (RK) that integrates land use types. Results showed that LMRK outperformed other models. The relationships between SOCD and landscape metrics were found to be scale-dependent, and the buffer of 300 m exhibited the optimal scale in our case. The LMRK also revealed that a highly connected and water-sufficient landscape was conducive to the accumulation of soil organic carbon in farmlands. These results indicated that landscape metrics serve as good predictors, and the proposed LMRK method is effective for SOCD mapping in plains. Our findings highlight the scale-dependent relationship between landscape metrics and SOCD and provide a new perspective for soil mapping in plains.