Spatial predictive modeling of soil organic carbon stocks in Norwegian forests

Boreal forest soils are a critical terrestrial carbon (C) reservoir, with soil organic carbon (SOC) stocks playing a key role in global C cycling. In this study, we generated high-resolution (16 m) spatial predictions of SOC stocks in Norwegian forests for three depth intervals: (1) soil surface down to 100 cm depth, (2) forest floor (LFH layer), and (3) 0-30 cm into the mineral soil. Our predictions were based on legacy soil data collected between 1988 and 1992 from a subset (n = 1014) of National Forest Inventory plots. We used boosted regression tree models to generate SOC estimates, incorporating environmental predictors such as land cover, site moisture, climate, and remote sensing data. Based on the resulting maps, we estimate total SOC stocks of 1.57-1.87 Pg C down to 100 cm, with 0.55-0.66 Pg C stored in the LFH layer and 0.68-0.80 Pg C in the upper mineral soil. These correspond to average SOC densities of 15.3, 5.4, and 6.6 kg C m^-2, respectively. We compared the predictive performance of these models with another set, supplemented by soil chemistry variables. These models showed higher predictive performance (R² = 0.65-0.71) than those used for mapping (R² = 0.44-0.58), suggesting that the mapping models did not fully capture environmental variability influencing SOC stock distributions. Within the spatial predictive models, Sentinel-2 Normalized Difference Vegetation Index, depth to water table, and slope contributed strongly, while soil nitrogen and manganese concentrations had major roles in models incorporating soil chemistry. Prediction uncertainties were related to soil depth, soil types, and geographical regions, and we compared the spatial prediction against external SOC data. The generated maps of this offer a valuable starting point for identifying forest areas in Norway where SOC may be vulnerable to climate warming and management-related disturbances, with implications for soil CO₂ emissions.