Spatial and Temporal Variations in Soil Organic Carbon in Northwestern China via Comparisons of Different Methods

Soil organic carbon (SOC) is a crucial component for investigating carbon cycling and global climate change. Accurate data exhibiting the temporal and spatial distributions of SOC are very important for determining the soil carbon sequestration potential and formulating climate strategies. An important scheme of mapping SOC is to establish a link between environmental factors and SOC via different methods. The Shiyang River Basin is the third largest inland river basin in the Hexi Corridor, which has closed geographical conditions and a relatively independent carbon cycle system, making it an ideal area for carbon cycle research in arid areas. In this study, 65 SOC samples were collected and 21 environmental factors were assessed from 2011 to 2021 in the Shiyang River Basin. The linear regression (LR) method and two machine learning methods, i.e., support vector machine regression (SVR) and random forest (RF), are applied to estimate the spatial distribution of SOC. RF is slightly better than SVR because of its advantages in the comparison of classification. When latitude, slope, and the normalized vegetation index (NDVI) are used as predictor variables, the best SOC performance is shown. Compared with the Harmonized World Soil Database (HWSD), the optimal scheme improved the accuracy of the SOC significantly. Finally, the spatial distribution of SOC tended to increase, with a total increase of 135.94 g/kg across the whole basin. The northwestern part of the middle basin decreased by 2.82% because of industrial activities. The SOC in Minqin County increased by approximately 62.77% from 2011 to 2021. Thus, the variability of the spatial SOC increased. This study provides a theoretical basis for the spatial and temporal distributions of SOC in inland river basins. In addition, this study can also provide effective and scientific suggestions for carbon projects, offer a key scientific basis for understanding the carbon cycle, and support global climate change adaptation and mitigation strategies.