Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area.

• We developed static SOC mapping to space–time SOC modeling during 1980–2015. • GWRK-RothC presented the higher prediction accuracy than GWRK and RothC. • Mechanisms of the RothC model can be integrated into the GWRK algorithm. • Integration of mechanistic model into the DSM can be extended to different areas. Soil organic carbon (SOC) in intensively human-impacted areas often presents extremely complicated patterns in space and time, which brought a new challenge to space–time SOC modelling. Here we attempt to create a space-for-time GWRK-RothC model by incorporating the predictions of the process-based RothC model into the geographically weighted regression kriging (GWRK) for improving the space–time modelling of SOC in intensively human-impacted area. We collected a total of 1219 topsoil samples (0–20 cm) from southern Jiangsu Province of China in 1980, 2000, and 2015, and modelled the spatiotemporal dynamics of SOC by using the modified RothC model (with a newly-added pH modifier for representing the impact of soil acidification on SOC decomposition), the space-for-time GWRK, and the proposed space-for-time GWRK-RothC model. Results showed that the change trends in SOC derived by the three methods were similar, with an overall trend of SOC increase during 1980–2000 and SOC decline in the subsequent 15 years (2000–2015). However, their performances for space–time SOC modelling were different. The proposed space-for-time GWRK-RothC model had the highest accuracy in representing the spatiotemporal evolution of SOC in the study area, with the lowest root mean square error (RMSE) (3.06 g kg−1), mean error (ME) (-0.19 g kg−1), and the highest coefficient of determination (R2) (0.61), compared to the accuracy of space-for-time GWRK (RMSE = 3.53 g kg−1, ME = -0.36 g kg−1, and R2 = 0.45) and RothC model (RMSE = 4.23 g kg−1, ME = 0.20 g kg−1, and R2 = 0.20). These results highlighted the importance of integrating process-model-derived SOC spatiotemporal dynamics information in space-for-time digital soil mapping (DSM) for improving the space–time SOC modelling. We believe that outputs from this study may offer valuable guidance for developing space–time DSM model to achieve better performance of SOC modelling in space and time, especially for areas affected by strong anthropogenic activities. [ABSTRACT FROM AUTHOR]