Spatial patterns of soil organic carbon stocks and its controls in Chinese grassland ecosystems

•This study estimated that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm, and the average SOC density was 4.69 kg C m−2.•A path network showing interacting effects of climate, topography, soil characteristics, and vegetation on SOC.•The direct effect of topography on SOC was relatively weak, but it did have a significant positive impact on vegetation at a broad geographical scale. Estimations of the patterns and controls of soil organic carbon (SOC) could provide instructive insights into the potential impact of future global change on soil carbon (C). In this work, we combined GeoDetector and random forest (RF) to estimate SOC stocks in Chinese grassland ecosystems with uncertainty assessments, and identified a network of cross-correlated environmental covariates for determining SOC based on a dataset collected from 813 sampling sites (0–20 cm) collected from 2000 to 2014. We predicted that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm and that the average SOC density was 4.69 kg C/m−2(−|−). The effectiveness of using RF to predict SOC was demonstrated by an accuracy assessment based on 10-fold cross-validation, with a ratio of performance to deviation (RPD) of 2.89. The SOC stocks in southern China were lower than those in northern China. A high SOC density was found in northeastern China and on the Qinghai–Tibet Plateau. Soil properties had the strongest direct effect on SOC. Climate was significantly negatively associated with SOC and indirectly affected SOC via its effect on soil properties. Topography had a significant direct impact on vegetation, but its direct effect on SOC was relatively weak. This study emphasizes the patterns and heterogeneity of SOC stocks as well as the relative significance of climate, vegetation, soil characteristics, topography, and their complex interrelationships in controlling SOC. These results may provide a theoretical foundation for developing sustainable management systems and calibrating C process models.