Spatial Differentiation Characteristics of the Soil Health Index in Heilongjiang Province, China and Implications for Zonal Management

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.