Spatial patterns and modelling of soil organic carbon stocks across the northern boreal landscape

Boreal soils are vital carbon reservoirs, playing a crucial role in the global carbon cycle. The northern boreal landscape is characterized by small-scale variations of forests, wetlands and fells, each with widely differing carbon stocks. These ecosystems are warming at rates significantly faster than the global average, making them a priority for climate change research. To better understand carbon dynamics, it is essential to investigate the factors influencing the magnitude and spatial distribution of carbon stocks.  

This study analyzed soil organic carbon (SOC) stocks, the environmental factors driving their spatial distribution, and the reliability of various modeling methods for SOC variability. The data set includes 217 soil organic matter and carbon content samples, total soil depth, soil organic layer thickness, and remote sensing data (land cover, topography, vegetation). SOC stocks were modeled and predicted across the entire study area using an ensemble of five approaches: Generalized Linear Models, Generalized Additive Models, Generalized Boosted Models, Support Vector Machines and Random Forest, validated with leave‑one‑out cross‑validation 

The results indicate that biotope, groundcover and soil wetness index are the primary factors influencing SOC variation, while secondary factors include slope, elevation and topographic position. Soil organic layer thickness ranges from 0.0 to 4.4 meters, with an average of 0.4 meters. The total estimated carbon stock for the 150 km² study area is approximately 1.86 Mt (14.3 kg/m²), with the highest stocks (205 kg/m²) found in aapa mire wetlands. The median leave-one-out cross validation result across the five methods was RMSE = 29.3, MAE = 14.0, and R² = 0.41. 

The study shows that fine‑scale variation in biotopes, groundcover and terrain‑driven wetness shapes SOC patterns across northern boreal landscapes. Valley‑bottom wetlands, especially aapa mires, hold exceptionally large carbon stocks and play a central role in the boreal carbon cycle. Robust carbon stock data is essential for improving climate predictions and guiding effective mitigation efforts.