Retrieval of moisture content of common Sphagnum peat moss species from hyperspectral and multispectral data

Peatlands store enormous amounts of carbon in a peat layer, the formation and preservation of which can only occur under waterlogged conditions. Monitoring peatland moisture conditions is critically important because a decrease in moisture leads to peat oxidation and the release of accumulated carbon back into the atmosphere as a greenhouse gas. Optical remote sensing enables the indirect monitoring of peatland moisture conditions by identifying moisture-driven changes in vegetation spectral signatures. The vegetation of northern peatlands is dominated by Sphagnum mosses, whose spectral signatures are known to be highly sensitive to changes in moisture content. In this study, we tested methods to estimate Sphagnum moisture content from spectral data using seven spectral moisture indices, the OPtical TRApezoid Model (OPTRAM) and the Continuous Wavelet Transform (CWT). This study was based on data representing nine Sphagnum species sampled from two habitats in southern boreal peatlands in Finland. Our results showed that both multi- and hyperspectral data can be used to estimate the moisture content of Sphagnum mosses. Nevertheless, the optimal retrieval method depended on habitat characteristics. Using hyperspectral data, we found that: (i) the CWT exhibited superior performance for all studied moss species (R_Marg²= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (R_Marg²= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (R_Marg²= 0.68, ICC = 0.55) for the ombrotrophic species. Furthermore, we demonstrated that using multispectral data instead of hyperspectral data provides comparable results in moisture estimation when used as input with OPTRAM or Moisture Stress Index (MSI). This approach could lead to new insights into the moisture dynamics in Sphagnum-dominated peatlands over the span of the multispectral satellite era.