Coastal blue carbon ecosystems play a pivotal role in mitigating global climate change through rapid sediment burial and efficient carbon preservation. However, accurate carbon accounting is severely hindered by the systematic neglect of soil bulk density (BD) variations and the uncritical reliance on terrestrial-derived pedotransfer functions, masking hidden uncertainties in regional carbon stock assessments. Here we present a high-resolution, multi-depth assessment of soil organic carbon (SOC) content and BD across a 1-m vertical gradient in the intensively managed coastal zone of Jiangsu Province, China. Machine learning frameworks reveal a striking spatial and vertical decoupling between SOC and BD driven by divergent environmental controls: SOC content responds to soil depth and ocean salinity, whereas BD aligns with hydro-geomorphic distance to the coast. Vegetation mediates a tight vertical negative coupling (p < 0.001) in natural salt marshes, whereas anthropogenic activities decouple this relationship in croplands, restricting standard pedotransfer function predictability in all layers (R² ≤ 0.22). Across all layers, conventional spatial estimation models yield high baseline prediction uncertainty (RMSE = 0.22 g cm⁻³). These findings demonstrate that independent, high-resolution BD profiling is indispensable for valid blue carbon verification. Our results establish a transferable baseline to optimize depth-specific sampling and refine global coastal carbon accounting models under intensifying human pressures.

Coastal wetlands are important blue carbon ecosystems that play a significant role in the global carbon cycle. However, there is insufficient understanding of the variations in soil organic carbon (SOC) stocks and the mechanisms driving these ecosystems. Here we analyze a comprehensive multi-source dataset of SOC in topsoil (0–20 cm) and subsoil (20–100 cm) across 31 coastal wetlands in China to identify the factors influencing their distribution. Structural equation models (SEMs) reveal that hydrology has the greatest overall effect on SOC in both soil layers, followed by vegetation, soil properties, and climate. Notably, the mechanisms driving SOC density differ between the two layers. In topsoil, vegetation type and productivity directly impact carbon density as primary sources of carbon input, while hydrology, primarily through seawater salinity, exerts the largest indirect influence. Conversely, in subsoil, hydrology has the strongest direct effect on SOC, with seawater salinity also influencing SOC indirectly through soil and vegetation mediation. Soil properties, particularly pH, negatively affect carbon accumulation, while climate influences SOC indirectly via its effects on vegetation and soil, with a diminishing impact at greater depths. Using Random Forest, we generate high-resolution maps (90 m × 90 m) of topsoil and subsoil carbon density (R² of 0.53 and 0.62, respectively), providing the most detailed spatial distribution of SOC in Chinese coastal wetlands to date. Based on these maps, we estimate that SOC storage to a depth of 1 m in Chinese coastal wetlands totals 74.58 ± 3.85 Tg C, with subsoil carbon storage being 2.5 times greater than that in topsoil. These findings provide important insights into mechanism on driving spatial pattern of blue carbon and effective ways to assess carbon status on a national scale, thus contributing to the advancement of global blue carbon monitoring and management.