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Boreal 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 genus Sphagnum mosses. We investigated how the reflectance spectra of common boreal Sphagnum mosses, 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 the Sphagnum species 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 of Sphagnum mosses collected in this study is available as open data and can be used to develop new methods for remote monitoring of boreal peatlands.

<p>Water table constitutes a master control of the general biogeochemistry in northern peatlands. The performance of peatland simulations in global ecosystem models is strongly hampered by the accuracy of the water table predictions. We examined the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over 53 intact, restored, and drained northern peatlands in Finland, Estonia, Sweden, Canada, and the USA from 2018 through 2021. Various OPTRAM were computed based on Sentinel-2 data with the Google Earth Engine cloud platform. We found that (i) the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance; (ii) the tree cover density is a significant factor controlling the sensitivity of OPTRAM to water table dynamics; (iii) the relationship between water table and OPTRAM often disappears for deep water tables. Based on an anomaly analysis, we further found that OPTRAM seems to be in particular suitable to monitor long-term (i.e., interannual) water table variability while the performance for short-term changes (e.g., response to individual rain events) was lower. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density when the water table is shallow to moderately deep.</p>

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