Peatlands mapped across the European Alps with a harmonised multi-sensor workflow show widespread degradation

Abstract Alpine peatlands are disproportionately large carbon sinks and important biodiversity reservoirs, yet their small size, fragmentation, ecological heterogeneity, and limited accessibility in complex mountain terrain hamper consistent mapping and monitoring. We developed a cross-country multi-sensor workflow for detecting 590 ground-reference peatlands larger than 1700 m 2 and identified areas of potential degradation across the European Alps. Combinations of Sentinel-2 optical features, Sentinel-1 backscatter, Landsat-8 surface temperature, and terrain metrics were used in a detection model. Performance metrics were estimated in a spatially blocked cross-validation. We show that the best-performing model is dominated by optical features from Sentinel-2. The model failed to detect peatlands from the ground-truth (official mappings)—especially where orthophotos reveal disturbances such as forest encroachment or agricultural use. Apparent “false positives” often coincide with plausible unmapped peatlands. This scalable, open workflow enables harmonised, cross-border peatland updates in complex mountain terrain and provides policy-ready products to target conservation and restoration where high-elevation carbon sinks and biodiversity hotspots are at risk.