Abstract

Peatlands are key reservoirs of belowground carbon (C) and their monitoring is important to assess the rapid changes in the C cycle caused by climate change and direct anthropogenic impacts. Frequently, information of peatland area and vegetation type estimated by remote sensing has been used along with soil measurements and allometric functions to estimate belowground C stocks. Despite the accuracy of such approaches, there is still the need to find mappable proxies that enhance predictions with remote sensing data while reducing field and laboratory efforts. Therefore, we assessed the use of aboveground vegetation attributes as proxies to predict peatland belowground C stocks. First, the ecological relations between remotely detectable vegetation attributes(i.e. vegetation height, aboveground biomass, species richness and floristic composition of vascular plants) and belowground C stocks were obtained using structural equation modeling (SEM). SEM was formulated using expert knowledge and trained and validated using in-situ information. Second, the SEM latent vectors were spatially mapped using random forest regressions with UAV-based hyperspectral and structural information. Finally, this enabled us to map belowground C stocks using the SEM functions parameterized with the random forests derived maps. This SEM approach resulted in higher accuracies than a direct application of a purely data-driven random forest approach with UAV data, with improvements of r2from 0.39 to 0.54, normalized RMSE from 31.33% to 20.24% and bias from −0.73 to 0.05. Our case study showed that: (1) vegetation height, species richness and aboveground biomass are good proxies to map peatland belowground C stocks, as they can be estimated using remote sensing data and hold strong relationships with the belowground C gradient; and (2) SEM is facilitated to incorporate theoretical knowledge in empirical modeling approaches.