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Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Verma M., Friedl Mark A., Richardson Andrew D., Kiely Gérard, Cescatti Alessandro, Law Bev, Wohlfahrt Georg, Gielen Bert, Roupsard Olivier, Moors Eddy J., Toscano Piero, Vaccari Francesco P., Gianelle Damiano, Bohrer Gil, Varlagin Andrej, Buchmann Nina, Van Gorsel Eva, Montagnani Leonardo, Propastin P.. 2014. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set. Biogeosciences, 11 (8) : pp. 2185-2200.

Journal article ; Article de revue à facteur d'impact Revue en libre accès total
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Matériel d'accompagnement : 1 fichier complémentaire "Online material" (8 p.)

Quartile : Q1, Sujet : GEOSCIENCES, MULTIDISCIPLINARY / Quartile : Q1, Sujet : ECOLOGY

Abstract : Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10-80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40-60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges. (Résumé d'auteur)

Mots-clés Agrovoc : Télédétection, Productivité primaire, Modèle de simulation, Modèle mathématique, Biomasse, Végétation, Forêt, Terre agricole, Cycle du carbone, Zone climatique, Changement climatique

Classification Agris : F40 - Plant ecology
P01 - Nature conservation and land resources
U10 - Computer science, mathematics and statistics
U30 - Research methods
B10 - Geography

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

  • Verma M., Boston University (USA)
  • Friedl Mark A., Harvard University (USA)
  • Richardson Andrew D., Harvard University (USA)
  • Kiely Gérard, UCC (IRL)
  • Cescatti Alessandro, IES (ITA)
  • Law Bev, Oregon State University (USA)
  • Wohlfahrt Georg, Institute of Ecology (AUT)
  • Gielen Bert, University of Antwerp (BEL)
  • Roupsard Olivier, CIRAD-PERSYST-UMR Eco&Sols (CRI)
  • Moors Eddy J., Alterra (NLD)
  • Toscano Piero, Institute of Biometeorology (ITA)
  • Vaccari Francesco P., Institute of Biometeorology (ITA)
  • Gianelle Damiano, IASMA (ITA)
  • Bohrer Gil, Ohio State University (USA)
  • Varlagin Andrej, Russian Academy of Sciences (RUS)
  • Buchmann Nina, ETH (CHE)
  • Van Gorsel Eva, ETH (CHE)
  • Montagnani Leonardo, Forest Services and Agency for the Environment (ITA)
  • Propastin P., Georg-August University (DEU)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/574130/)

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