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• The relationships between SOCD and landscape metrics are scale-dependent. • A connected and water-sufficient landscape is conducive to accumulating SOC. • The proposed LMRK model is effective for SOCD mapping in plains. 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. [ABSTRACT FROM AUTHOR]

• Geographical detectors (GD) are used to guide the stratification. • Stratified regression kriging (SRK) improves the prediction accuracy. • The spillover effect of land use on the SOC content depends on the strata. • The GD-SRK strategy effectively maps SOC with high spatial heterogeneity. Spatial variability of soil organic carbon (SOC) content is crucial in precision fertilization and climate change prediction. Stratified ordinary kriging (SOK), which considers the sharp spatial variability of SOC due to natural factors and human activities, is a feasible way to map SOC with high spatial heterogeneity. However, conventional stratified methods are arbitrary and may therefore result in unnecessary and ineffective stratification. In addition, SOK performs poorly when soil properties of some strata present a local trend. In this study, we propose a geographical detector-based stratified regression kriging (GD–SRK) strategy, which aims to guide the stratification, gain strata-dependent knowledges, and improve the performance of stratified kriging. The study area locates in Jianghan Plain, China. The soil and land use types are used as categorical variables, while land use percentages are used as continuous variables. Ordinary kriging (OK), regression kriging (RK), and geographical detector-based stratified ordinary kriging are used as references of GD–SRK. Results indicated that the spatial heterogeneity of SOC was attributed to the differences in soil types and land use types and suggested that the study area should be stratified into four. The spillover effect of land use on the SOC content depended on the strata. Specifically, the SOC in strata II (paddy fields ∩ Fluvisols) and IV (irrigated lands ∩ Fluvisols) was unaffected by land use percentages and was estimated via OK. The SOC in strata I (paddy fields ∩ Anthrosols and Luvisols) and III (irrigated lands ∩ Anthrosols and Luvisols) was negatively correlated with the percentage of irrigated lands. Thus, their estimation of SOC via RK was possible. Using these knowledges, the GD–SRK outperformed the other methods by obtaining highest prediction accuracy and R2. We conclude that GD–SRK strategy effectively guides the stratification and maps SOC with high spatial heterogeneity. [ABSTRACT FROM AUTHOR]