AbstractBoreal peatlands store ~25 % of global soil organic carbon and host many endangered species; however, they face degradation due to climate change and anthropogenic drainage. In boreal peatlands, vegetation indicates ecohydrological conditions of the ecosystem. Applying remote sensing would enable spatially and temporally continuous monitoring of peatland vegetation. New multi‐ and hyperspectral satellite data offer promising approaches for understanding the spectral properties of peatland vegetation at high temporal and spectral resolutions. However, using spectral satellite data to their fullest potential requires detailed spectral analyses of dominant species in peatlands. A dominant feature of peatland vegetation is the genusSphagnummosses. We investigated how the reflectance spectra of common borealSphagnummosses, collected from waterlogged natural conditions after snowmelt, change when the mosses are desiccated. We conducted a laboratory experiment where the reflectance spectra (350–2500 nm) and the mass of 90 moss samples (representing nine species) were measured repetitively. Furthermore, we examined (i) their inter‐ and intraspecific spectral differences and (ii) whether the species or their respective habitats could be identified based on their spectral signatures in varying states of drying. Our findings show that the most informative spectral regions to retrieve information about theSphagnumspecies and their state of desiccation are in the shortwave infrared region. Furthermore, the visible and near‐infrared spectral regions contain less information on species and moisture content. Our results also indicate that hyperspectral data can, to a limited extent, be used to separate mosses belonging to meso‐ and ombrotrophic habitats. Overall, this study demonstrates the importance of including data especially from the shortwave infrared region (1100–2500 nm) in remote sensing applications of boreal peatlands. The spectral library ofSphagnummosses collected in this study is available as open data and can be used to develop new methods for remote monitoring of boreal peatlands.