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High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling

Liang, Zongzheng, Chen, Songchao, Yang, Yuanyuan, Zhou, Yue, Shi, Zhou
The Science of the total environment 2019 v.685 pp. 480-489
carbon cycle, climate change, cropland, environmental models, forests, grasslands, prediction, soil depth, soil fertility, soil formation, soil organic carbon, soil profiles, soil properties, soil surveys, tiles, uncertainty, China
Soil organic carbon (SOC) is a key factor in soil fertility and structure and plays an important role in the global carbon cycle. However, SOC causes a large uncertainty in Earth System Models for predicting future climate change. The GlobalSoilMap (GSM) project aims to provide global digital soil maps of primary functional soil properties at six standard depth intervals (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) with a grid resolution of 90 × 90 m. Currently, few SOC national products that meet the GSM specifications are available. This study describes the three-dimensional spatial modeling of SOC maps according to GSM specifications. We used 5982 soil profiles collected during the Second National Soil Survey of China, along with 16 environmental covariates related to soil formation. The results were obtained by parallel computing over tiles of 100 × 100 km, and the predictions for the tiles were subsequently merged into a single SOC map for the whole of China per standard GSM depth interval. For each standard GSM depth interval, SOC contents and their uncertainties were predicted and mapped at a spatial resolution of approximately 90 m using bootstrapping. Southwestern and northeastern China had higher SOC contents than the rest of China did, whereas northwestern China had a lower SOC content. The range of the coefficient of determination for the six depth intervals ranged from 0.35 to 0.02, and the mean SOC content was 17.86–8.67 g kg−1. Both these values decreased strongly with increasing soil depth. Cropland SOC content was lower than that of forest and grassland. The results of variable importance show that SoilGrids data were the best predictors for defining the soil-landscape relationship during regression modeling for SOC. These SOC maps can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor SOC dynamics, and a guide for the design of future soil surveys.