Investigating the spatio-temporal variability of soil organic carbon stocks in different ecosystems of China.

Soil organic carbon (SOC) significantly influences soil fertility, soil water holding capacity, and plant productivity. In this study, we applied two boosted regression tree (BRT) models to map SOC stocks across China in the 1980s and the 2010s. The models incorporated nine environmental variables (climate, topography, and biology) and 8897 (in the 1980s) and 4534 (in the 2010s) topsoil (0-20 cm) samples. During the two study periods, 20% of the soil samples were randomly selected for model testing, and the remaining samples were used as a training set to construct the models. The verification results showed that incorporating climate environment variables significantly improved the model prediction in both study periods. Mean annual temperature, mean annual precipitation, elevation, and the normalized difference vegetation index were the dominant environmental factors affecting the spatial distribution of SOC stocks. The full-variable model predicted similar spatial distributions of SOC stocks for the 1980s and the 2010s. SOC stocks in China showed an increasing trend over the past 30 years, from 3.9 kg m in the 1980s to 4.6 kg m in the 2010s. In both periods, topsoil SOC stocks were mainly stored in agroecosystems, forests, and grasslands in the 1980s, with values of 9.5, 12.0, and 11.4 Pg C, respectively. Our study provides reliable information on Chain's carbon distribution, which can be used by land managers and the national government to formulate relevant land use and carbon sequestration policies.