In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to predict the SOC from 1989 to 2018, and the geographic detector method (GDM) was applied to analyze quantitatively the natural and anthropogenic factors that are affecting Aohan banner. The results indicated that: (1) After adding the terrain factors, the R² and residual predictive deviation (RPD) of the RF model increased by 1.178 and 0.39%, with root mean square errors (RMSEs) of 1.42 g/kg and 1.05 g/kg, respectively; (2) The spatial distribution of SOC was higher in the south and lower in the north; the negative growth of SOC accounted for 55.923% of the total area, showing a trend of degradation; (3) Precipitation was the main driving factor of SOC spatial variation in the typical agro-pastoral ecotone of northern China, which was also affected by temperature, elevation, soil type and soil texture (p < 0.01). (4). Anthropogenic factors (carbon input and gross domestic product (GDP)) had a greater impact on SOC than did climate factors (temperature and precipitation), making anthropogenic factors the dominant factors affecting SOC temporal variation (p < 0.01). The results of this work constitute a basis for a regional assessment of the temporal evolution of organic carbon in the soil surface, which is a key tool for monitoring the sustainable development of agropastoral ecotones.

Grassland is an important ecosystem; the spatiotemporal evolution trend of grassland and its impacts on climatic factors play an irreplaceable role in maintaining regional sustainable development and ecological balance. In this paper, based on the remote sensing images of 1990, 2000, 2010, 2020, and 3S technology, we use the methods of dynamic rate and transfer matrix to analyze the spatiotemporal evolution trend of the northern Songnen Plain (China). The method of grey correlation is used to analyze the impact of climate factors on it. The results showed that the grassland changed dramatically and unevenly across the three periods of 1990–2000, 2000–2010, and 2010–2020, with the biggest change in the last period. The internal conversion of grassland mainly occurred between H-grassland (high coverage grassland) and M-grassland (medium coverage grassland), while the transformation rarely occurred in L-grassland (low coverage grassland) due to its small area. There has been a transfer-in from cultivated land, woodland, and unused land to H-grassland and M-grassland. The grassland transfer-out was mainly from H-grassland and M-grassland to cultivated land and unused land. What’s more, the transformation mainly occurred in Daqing City, Suihua City, Qiqihar city, as well as occurring in the west of Harbin and the southwest of Heihe city. Climate change has exacerbated the reduction of grassland areas. In summary, the spatiotemporal change rates of grassland area in the north of Songnen Plain initially showed a mild decrease and were then followed by a rapid decrease. Climate factors were of great significance to the spatiotemporal changes of grassland, and precipitation had a greater impact on the reduction of grassland. The results can provide meaningful information for grassland change, grassland protection, and management in the northern Songnen Plain.

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.