Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects

Abstract

Our understanding of the terrestrial carbon cycle has been greatly enhanced since satellite observations of the land surface started. The advantage of remote sensing is that it provides wall-to-wall observations including in regions where in situ monitoring is challenging. This paper reviews how satellite observations of the biosphere have helped improve our understanding of the terrestrial carbon cycle. First, it details how remotely sensed information of the land surface has provided new means to monitor vegetation dynamics and estimate carbon fluxes and stocks. Second, we present examples of studies which have used satellite products to evaluate and improve simulations from global vegetation models. Third, we focus on model data integration approaches ranging from bottom-up extrapolation of single variables to carbon cycle data assimilation system able to ingest multiple types of observations. Finally, we present an overview of upcoming satellite missions which are likely to further improve our understanding of the terrestrial carbon cycle and its response to climate change and extremes.

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Acknowledgements

This review stemmed from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” at the International Space Science Institute in Bern during November 2017. Contribution from AAB was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The first and last authors were supported by the Natural Environment Research Council through the National Centre for Earth Observation, contract number PR140015, and the Newton Fund, through CSSP Brazil.

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Exbrayat, J., Bloom, A.A., Carvalhais, N. et al. Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects. Surv Geophys 40, 735–755 (2019). https://doi.org/10.1007/s10712-019-09506-2

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Keywords

  • Terrestrial carbon cycle
  • Earth observation
  • Satellite
  • Ecosystem modelling
  • Model data integration