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

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https://ir.library.oregonstate.edu/concern/articles/wh246t93p

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  • 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.
  • This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net/.
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  • 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, P., Gianelle, D., Bohrer, G., Varlagin, A., Buchmann, N., van Gorsel, E., Montagnani, L., and Propastin, P. (2014). 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
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  • 11
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  • 8
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  • This research was partially supported byNASA grant number NNX11AE75G, the National Science FoundationMacrosystem Biology program (award EF-1065029), andAmeriFlux [the Office of Science (BER), US Department of Energy(DOE; DE-FG02-04ER63917 and DE-FG02-04ER63911)].This work used eddy covariance data acquired by the FLUXNETcommunity and in particular by the following networks: AmeriFlux(US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 andDE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP,CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada(supported by CFCAS, NSERC, BIOCAP, Environment Canada,and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOSSiberia,USCCC. We acknowledge the financial support for theeddy covariance data harmonization provided by CarboEuropeIP,FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry,National Science Foundation, University of Tuscia, UniversitéLaval, Environment Canada and US Department of Energy andthe database development and technical support from BerkeleyWaterCenter, Lawrence Berkeley National Laboratory, Microsoft ResearcheScience, Oak Ridge National Laboratory, University of California– Berkeley, and the University of Virginia.
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