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Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 615, Issue -, Pages 857-874

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2017.09.103

Keywords

Satellites; GPP; Respiration; NEE; Vegetation indices; Restored peatlands

Funding

  1. James Hutton Institute
  2. Natural Environment Research Council (NERC) SCENARIO DTP [NE/L002566/1]
  3. NERC National Centre for Earth Observation (NCEO)
  4. Scottish Government Strategic Research Programme
  5. NERC [nceo020004] Funding Source: UKRI
  6. Natural Environment Research Council [nceo020004, NE/R016518/1] Funding Source: researchfish

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Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration. (C) 2017 The Authors. Published by Elsevier B.V.

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