| Creator |
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Verma, M.
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Friedl, M. A.
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Richardson, A. D.
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Kiely, G.
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Cescatti, A.
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Law, B. E.
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Wohlfahrt, G.
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Gielen, B.
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Roupsard, O.
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Moors, E. J.
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Toscano, P.
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Vaccari, P.
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Gianelle, D.
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Bohrer, G.
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Varlagin, A.
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Buchmann, N.
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van Gorsel, E.
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Montagnani, L.
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Propastin, P.
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| 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.
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| 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, 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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| Rights Statement |
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| Funding Statement (additional comments about funding) |
- This research was partially supported by
NASA grant number NNX11AE75G, the National Science Foundation
Macrosystem Biology program (award EF-1065029), and
AmeriFlux [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 FLUXNET
community and in particular by the following networks: AmeriFlux
(US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and
DE-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 the
eddy covariance data harmonization provided by CarboEuropeIP,
FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry,
National Science Foundation, University of Tuscia, Université
Laval, Environment Canada and US Department of Energy and
the database development and technical support from BerkeleyWater
Center, Lawrence Berkeley National Laboratory, Microsoft Research
eScience, Oak Ridge National Laboratory, University of California
– Berkeley, and the University of Virginia.
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| Additional Information |
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