The black soil region of northeastern China, one of the world’s major black soil belts, is China’s main grain-producing area, producing a quarter of China’s commercial grain. However, over-exploitation and unsustainable management practices have led to a steady decline in the quality of arable land. Scientific and reasonable zoning of arable land is the key to ensuring that black soil arable land achieves sustainable development. In this study, the 317 districts and counties under the jurisdiction of Heilongjiang, Jilin, and Liaoning Provinces in the northeast region and the four eastern leagues of the Inner Mongolia Autonomous Region were taken as the study area, and arable land zoning in the northeast black soil region was explored through group analysis. Ten types of indicators were selected according to the four levels of climate, soil, vegetation, and topography of the northeast black soil region, including average precipitation and average temperature for many years at the climate level, organic matter content and soil texture (including clay, silt, and sand) at the soil level, NDVI and EVI indicators at the vegetation level, and DEM and slope indicators at the topographic level. In accordance with the principle of distinguishing differences and summarizing commonalities, nine scenarios of dividing the northeast black soil zones into 2 regions to 10 regions were explored, and these nine zoning scenarios were evaluated in terms of zoning. The results showed that (1) the spatial variability of cropland zoning in the northeast black soil zone based on four indicators, namely climate, soil, vegetation, and topography, was significant; (2) the results of the nine types of zoning based on cropland in the northeast black soil zone showed that intra-zonal zoning was optimal when zoning the northeast black soil zone into six types of zones, which enhanced the variability between the zones and the consistency within the zones; and (3) the assessment of large-scale cropland zoning using the pseudo F-statistic and area-weighted standard deviation methods revealed similarities in their outcomes. The results provide a scientific basis for the subregional protection of arable land in the black soil zone and help to formulate effective policies for different regions.

In recent 50 years, because of the interference of natural factors and human activities, the quality, productivity and the carrying capacity were decreased of Dumeng Country grassland in Heilongjiang Province. According to the division of county, Dumeng County township is determined disturbed grassland and none disturbed grassland in this study. Using sensing data of satellite remote, which is getting unit NDVI integral of every land. Analyzing the influence of human factors on grassland productivity by calculating the grassland relative degradation index. Finally, the relative degradation index and climate of the grassland productivity and human influence factors were analyzed. According to the result of grassland degradation index, combined with the villages and towns cultural statistics, found out the main human factors and affected the grassland productivity. Results show that the main human factors affect the Dumeng county grass productivity is the total population and the number of large livestock. And the principal component analysis results show that the load of the first and second principal components reached 71.635%; The correlation coefficient of Dumeng county total population and relative grass degradation index reached 0.733. The correlation coefficient of the number of large livestock and relative grass degradation index reached 0.603. The correlation level both reached 0.01 level. According to two regression methods based on the relative degradation index comparison, found that the curve regression method for Dumeng county each villages and towns of grassland degradation index of fitting effect is good, the main human factors affecting the majority of villages and towns grassland productivity is the total population.

Soil health is essential for food security, ecosystem stability, and sustainable development, yet its spatial heterogeneity and driving mechanisms remain insufficiently understood at regional scales. This study investigates soil health in Heilongjiang Province, China. A Soil Health Index (SHI) was constructed using eight indicators covering physical, chemical, and biological properties based on multi-source datasets at 1 km spatial resolution. A random forest (RF) model was applied to identify key environmental drivers, and Moran’s I and Getis–Ord Gi* statistics were used to analyze spatial clustering. The results showed that SHI values ranged from 0.19 to 0.70, with a mean of 0.45. The RF model achieved strong performance (R² = 0.6666, RMSE = 0.03184, MAE = 0.02372), significantly outperforming linear regression (R² ≈ 0.17). Significant spatial clustering was observed, where “hotspots” refer to statistically significant clusters of high SHI values, and “coldspots” indicate clusters of low SHI values based on Getis–Ord Gi* analysis. Climate factors (temperature and precipitation) and elevation were the dominant drivers. Significant spatial clustering was observed, with clear hotspot and coldspot patterns. These findings provide spatial evidence for sustainable land-use planning and zonal soil management. However, the analysis is limited by data resolution and model interpretability, which may affect the representation of fine-scale variability.