Peatlands are unique ecosystems with high biodiversity and environmental services such as water filtration and retention as well as carbon storage. Interestingly, however, in contrast to other soils and ecosystems, little is known about the extent and health of European peatlands (Andersen et al., 2017). With increasing human-induced drainage, degradation and restoration, there is an even greater need to monitor the extent and health of European peatlands (Andersen et al., 2017). We developed a conceptual framework to (1) distinguish between (unforested) peatlands and surrounding areas (forest and grassland), and (2) separate drained/degraded from natural/rewetted peatlands. Our study includes 11 European peatlands across three Köppen-Geiger climate classes (Kottek et al., 2006). We use remote sensing data because they provide objective, spatially explicit and temporally extensive data (Chasmer et al., 2020). We use Sentinel 2 and Planet Scope optical bands with high spatial and temporal resolution, focusing on red, red edge, near infrared (NIR), and shortwave infrared (SWIR) band reflectances to discriminate between peatland vegetation and surrounding areas (Burdun et al., 2023). Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index Red (EVI). Green Normalized Vegetation Index (gNDVI), and Greenness Index (GI) were used as indicators of vegetation composition and health (Burdrun et al., 2023; Räsänen et al, 2022), while Normalized Moisture Index (NDMI) was used as measure for vegetation water stress (Räsänen et al., 2022). Ground truthing of our data was performed with biogeochemical analyses, including pyrolysis gas chromatography with integrated mass spectroscopy (PYGCMS) to study the molecular composition of surface soils. In particular, we investigated molecules specific to different vegetation classes and their transformation products. To verify our results, we also used established biogeochemical parameters such as C:N ratio and oxidation state (Cox), which are indicators of the degree of microbial transformation and decomposition processes in soils (Leifeld et al., 2020). As a first step, we separated peatlands from surrounding forest using existing European Forest layers. We further distinguished grasslands from peatlands using red edge and NIR reflectance data, which were significantly higher for grasslands than for peatlands (p<0.001). To distinguish between natural/rewetted and degraded/drained sites, we will correlate the specific reflectance with the molecular and biochemical data to establish a framework for an inventory of peatland sites and their health on a regional scale using a machine learning approach.