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dc.contributor.author
Verma, Manish
dc.contributor.author
Friedl, Mark A.
dc.contributor.author
Richardson, Andrew D.
dc.contributor.author
Kiely, Gerard K.
dc.contributor.author
Kiely, Gerard K.
dc.contributor.author
Law, Beverly E.
dc.contributor.author
Wohlfahrt, Georg
dc.contributor.author
Gielen, Birgit
dc.contributor.author
Roupsard, Olivier
dc.contributor.author
Moors, Eddy J.
dc.contributor.author
Toscano, Piero
dc.contributor.author
Vaccari, Francesco Primo
dc.contributor.author
Gianelle, Damiano
dc.contributor.author
Bohrer, Gil
dc.contributor.author
Varlagin, Andrej
dc.contributor.author
Buchmann, Nina
dc.contributor.author
Gorsel, Eva van
dc.contributor.author
Montagnani, Leonardo
dc.contributor.author
Propastin, Pavel A.
dc.date.accessioned
2018-09-19T09:30:42Z
dc.date.available
2017-06-11T12:14:19Z
dc.date.available
2018-09-19T09:30:42Z
dc.date.issued
2014
dc.identifier.issn
1726-4170
dc.identifier.issn
1726-4170
dc.identifier.other
10.5194/bg-11-2185-2014
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/88791
dc.identifier.doi
10.3929/ethz-b-000088791
dc.description.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.
en_US
dc.format
application/pdf
dc.language.iso
en
en_US
dc.publisher
Copernicus
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Remote sensing of annual terrestrial gross primary productivity from MODIS: An assessment using the FLUXNET La Thuile data set
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2014-04-17
ethz.journal.title
Biogeosciences
ethz.journal.volume
11
en_US
ethz.journal.issue
8
en_US
ethz.pages.start
2185
en_US
ethz.pages.end
2200
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
006289717
ethz.publication.place
Göttingen
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03648 - Buchmann, Nina / Buchmann, Nina
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03648 - Buchmann, Nina / Buchmann, Nina
ethz.relation.isNewVersionOf
10.3929/ethz-b-000077629
ethz.date.deposited
2017-06-11T12:14:54Z
ethz.source
ECIT
ethz.identifier.importid
imp59365242c986b72811
ethz.ecitpid
pub:139706
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T14:53:46Z
ethz.rosetta.lastUpdated
2021-02-15T01:48:02Z
ethz.rosetta.versionExported
true
ethz.COinS
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