Assimilating satellite-based canopy height within an ecosystem model to estimate aboveground forest biomass

E. Joetzjer*, M. Pillet, P. Ciais, N. Barbier, J. Chave, M. Schlund, F. Maignan, Jonathan Barichivich, S. Luyssaert, B. Hérault, F. von Poncet, B. Poulter

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review

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    Abstract

    Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high-resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE-CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE-CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.

    Original languageEnglish
    Pages (from-to)6823-6832
    Number of pages10
    JournalGeophysical Research Letters
    Volume44
    Issue number13
    Early online date5 Jul 2017
    DOIs
    Publication statusPublished - 16 Jul 2017

    Funding

    The authors are grateful to Philippe Peylin for helpful discussions and to Victoria Meyer and Sassan Saatchi for providing lidar data and acknowledge the Gordon and Betty Moore Foundation NERC Consortium Grants “AMAZONICA” (NE/F005806/1), the “Investissement d'Avenir” grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP: ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001), and the European Union Climate KIC grant FOREST Specific Grant Agreement EIT/CLIMATE KIC/SGA2016/1CNES (TOSCA program) for funding. P.C. received support from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE. P.J.B. received funding from (CR)2 Chile (CONICYT/FONDAP/15110009). To access the remote sensing data, please contact Nicolas Barbier (nicolas.barbier@ird.fr) for Pleiades, Felicitas von Poncet (felicitas.poncet@airbus.com) for TanDEM-X, and Jerôme Chave (jerome.chave@univ-tlse3.fr) for lidar. We thank Bayani Cardenas, Martin Thurner, and one anonymous reviewer for critical reviews of the manuscript. The authors dedicate this paper to our colleague and dear friend Nicolas Najdovski.

    FundersFunder number
    CONICYT/FONDAP/15110009
    European Union Climate KICEIT/CLIMATE KIC/SGA2016/1CNES
    Seventh Framework Programme610028
    European Research Council
    Agence Nationale de la RechercheANR-11-INBS-0001, ANR-10-LABX-0041, ANR-10-LABX-25-01

      Keywords

      • Biomass
      • large-scale ecosystem model
      • optical satellite imagery
      • radar satellite imagery

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