Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes

Peatlands are Earth’s most carbon-dense terrestrial ecosystems and their carbon density varies with the depth of the peat layer. Accurate mapping of peat depth is crucial for carbon accounting and land management, yet existing maps lack the resolution and accuracy needed for these applications. This study evaluates whether digital soil mapping using remotely sensed data can improve existing maps of peat depth in western and southeastern Norway. Specifically, we assessed the predictive value of lidar-derived terrain variables and airborne radiometric data across two, > 10 km2 sites. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution. The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Terrain variables were better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. Radiometric variables showed inconsistent and weak predictive value – improving performance at one site while degrading it at the other. Our remote sensing models had better accuracy than the national map of peat depth, even when we calibrated the national map to the same depth data. Still, weak relationships with remotely sensed variables made peat depth hard to predict overall. Based on these findings, we conclude that digital soil mapping can improve the existing, national map of peat depth in Norway, but detailed local maps are best made from tailored field measurements. Together, these pathways promise more accurate landscape-scale carbon stock assessments and better-informed land management policies. © Author(s) 2025. This work is distributed under.