Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data.

The accurate mapping of farmland soil organic carbon density (SOCD) is crucial for evaluating carbon (C) sequestration potential and forecasting climate change. Natural factors such as soil types and topographical factors are important variables in mapping soil properties. Moreover, cropping systems are important components of agricultural activities and are significantly correlated with soil properties. Therefore, integrating cropping systems and natural factors can improve the accuracy of mapping farmland SOCD. This study aimed to obtain and incorporate cropping system information in mapping SOCD in plains by combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging (RK) method. We collected 230 topsoil samples in Jianghan Plain, China and (i) obtained the spatial patterns of crops in summer and winter using NDVI time-series data derived from HJ-1A/1B satellite images, (ii) investigated the differences in SOCD under different cropping systems, and (iii) evaluated the performance of the RK_CS model in integrating cropping systems and natural factors into mapping SOCD. ANOVA results showed significant differences in SOCD under different cropping systems. Specifically, the SOCD of single rice was higher than that of rice-wheat rotation and dry crops. Meanwhile, the regression results showed that SOCD was affected by natural factors and cropping system, with the latter playing a major role. The integration of soil types, slope and cropping systems explained approximately 26.3% of the variation in SOCD. Model validation results confirmed the effectiveness of the RK_CS model. The findings reveal single cropping rice sequences more C than other cropping systems. Cropping system is an important environmental variable in improving mapping farmland SOCD in plains.