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Biogeosciences, 11, 2185-2200, 2014
www.biogeosciences.net/11/2185/2014/
doi:10.5194/bg-11-2185-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set
M. Verma1, M. A. Friedl1, A. D. Richardson2, G. Kiely3, A. Cescatti4, B. E. Law5, G. Wohlfahrt6, B. Gielen7, O. Roupsard8,9, E. J. Moors10, P. Toscano11, F. P. Vaccari11, D. Gianelle12,13, G. Bohrer14, A. Varlagin15, N. Buchmann16, E. van Gorsel17, L. Montagnani18,19, and P. Propastin20
1Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
2Department of Organismic and Evolutionary Biology, Harvard University, HUH, 22 Divinity Avenue, Cambridge, MA 02138, USA
3Environmental Research Institute, Civil and Environmental Engineering Department, University College, Cork, Ireland
4European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra, Italy
5Earth Systems Science Division, Oregon State University, Corvallis, OR 97331, USA
6Institute of Ecology, University of Innsbruck, Sternwartestr 15, 6020 Innsbruck, Austria
7Research Group of Plant and Vegetation Ecology, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
8CIRAD, UMR Eco&Sols (Ecologie Fonctionnelle & Biogéochimie des Sols & Agroécosystèmes), 34000 Montpellier, France
9CATIE (Tropical Agricultural Centre for Research and Higher Education), 7170 Turrialba, Costa Rica
10Climate Change & Adaptive Land and Water Management, Alterra Wageningen UR, P.O. Box 47, 6700 AA Wageningen, the Netherlands
11Institute of Biometeorology (IBIMET – CNR), via G.Caproni 8, 50145 Firenze, Italy
12Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Center, Fondazione Edmund Mach, 38010 S. Michele all' Adige Trento, Italy
13FOXLAB, Research and Innovation Center, Fondazione Edmund Mach, San Michele, all'Adige, TN, Italy
14Department of Civil, Environmental & Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
15A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Lenisky pr.33, Moscow, 119071, Russia
16Institute of Agricultural Sciences, ETH Zurich, Universitatsstr. 2, 8092 Zurich, Switzerland
17CSIRO, Marine and Atmospheric Research, P.O. Box 1666, Canberra, ACT 2601, Australia
18Faculty of Science and Technology, Free University of Bolzano-Bozen, Italy
19Forest Services, Autonomous Province of Bolzano, Bolzano, Italy
20Institute of Bioclimatology, Georg-August University Göttingen, Büsgenweg 237077, Göttingen, Germany

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.

Citation: Verma, M., Friedl, M. A., Richardson, A. D., Kiely, G., Cescatti, A., Law, B. E., Wohlfahrt, G., Gielen, B., Roupsard, O., Moors, E. J., Toscano, P., Vaccari, F. P., Gianelle, D., Bohrer, G., Varlagin, A., Buchmann, N., van Gorsel, E., Montagnani, L., and Propastin, P.: Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set, Biogeosciences, 11, 2185-2200, doi:10.5194/bg-11-2185-2014, 2014.
 
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