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| Titel |
Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set |
| VerfasserIn |
M. Verma, M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, B. Gielen, O. Roupsard, E. J. Moors, P. Toscano, F. P. Vaccari, D. Gianelle, G. Bohrer, A. Varlagin, N. Buchmann, E. van Gorsel, L. Montagnani, P. Propastin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 8 ; Nr. 11, no. 8 (2014-04-17), S.2185-2200 |
| Datensatznummer |
250117368
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| Publikation (Nr.) |
 copernicus.org/bg-11-2185-2014.pdf |
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| Zusammenfassung |
| 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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