摘要 : 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 G... 展开 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 (R-2) (0.61), compared to the accuracy of space-for-time GWRK (RMSE = 3.53 g kg(-1), ME = -0.36 g kg(-1), and R-2 = 0.45) and RothC model (RMSE = 4.23 g kg(-1), ME = 0.20 g kg(-1), and R-2 = 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. 收起