Using spectral indices to estimate water content and GPP in Sphagnum moss and other peatland vegetationLees, K. J., Artz, R. R. E., Khomik, M., Clark, J. M. ORCID: https://orcid.org/0000-0002-0412-8824, Ritson, J., Hancock, M. H., Cowie, N. R. and Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613 (2020) Using spectral indices to estimate water content and GPP in Sphagnum moss and other peatland vegetation. IEEE Transactions on Geoscience and Remote Sensing, 58 (7). pp. 4547-4557. ISSN 0196-2892
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/TGRS.2019.2961479 Abstract/SummaryPeatlands provide important ecosystem services including carbon stroage and biodiversity conservation. Remote sensing shows potential for monitoring peatlands, but most off-the-shelf data produces are developed for unsaturated environments and it is unclear how well they can perform in peatland ecosystems. Sphagnum moss is an important peatland genus with specific characteristics which can affect spectral reflectance, and we hypothesized that the prevalence of Sphagnum in a peatland could affect the spectral signature of the area. This study combines results from both laboratory and field experiments to assess the relationship between spectral indices and the moisture content and GPP of peatland (blanket bog) vegetation species. The aim was to consider how well the selected indices perform under a range of conditions, and whether Sphagnum has a significant impact on the relationships tested. We found that both water indices tested (NDWI and fWBI) were sensitive to the water content changes in Sphagnum moss in the laboratory, and there was little difference between them. Most of the vegetation indices tested (the NDVI, EVI, SIPI and CIm) were found to have a strong relationship with GPP both in the laboratory and in the field. The NDVI and EVI are useful for large-scale estimation of GPP, but are sensitive to the proportion of Sphagnum present. The CIm is less affected by different species proportions and might therefore be the best to use in areas where species cover is unknown. The PRI is shown to be best suited to small-scale studies of single species.
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