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| Titel |
Predicting landscape-scale CO2 flux at a pasture and rice paddy with long-term hyperspectral canopy reflectance measurements |
| VerfasserIn |
J. H. Matthes, S. H. Knox, C. Sturtevant, O. Sonnentag, J. Verfaillie, D. Baldocchi |
| 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 ; 12, no. 15 ; Nr. 12, no. 15 (2015-08-03), S.4577-4594 |
| Datensatznummer |
250118046
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| Publikation (Nr.) |
 copernicus.org/bg-12-4577-2015.pdf |
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| Zusammenfassung |
| Measurements of hyperspectral canopy reflectance provide a detailed snapshot
of information regarding canopy biochemistry, structure and physiology. In
this study, we collected 5 years of repeated canopy hyperspectral
reflectance measurements for a total of over 100 site visits within the flux
footprints of two eddy covariance towers at a pasture and rice paddy in
northern California. The vegetation at both sites exhibited dynamic
phenology, with significant interannual variability in the timing of
seasonal patterns that propagated into interannual variability in measured
hyperspectral reflectance. We used partial least-squares regression (PLSR)
modeling to leverage the information contained within the entire canopy
reflectance spectra (400–900 nm) in order to investigate questions regarding
the connection between measured hyperspectral reflectance and
landscape-scale fluxes of net ecosystem exchange (NEE) and gross primary
productivity (GPP) across multiple timescales, from instantaneous flux to
monthly integrated flux. With the PLSR models developed from this large
data set we achieved a high level of predictability for both NEE and GPP flux
in these two ecosystems, where the R2 of prediction with an independent
validation data set ranged from 0.24 to 0.69. The PLSR models achieved the
highest skill at predicting the integrated GPP flux for the week prior to
the hyperspectral canopy reflectance collection, whereas the NEE flux often
achieved the same high predictive power at daily to
monthly integrated flux timescales. The high level of predictability
achieved by PLSR in this study demonstrated the potential for
using repeated hyperspectral canopy reflectance measurements to help
partition NEE into its component fluxes, GPP and ecosystem
respiration, and for using quasi-continuous hyperspectral reflectance
measurements to model regional carbon flux in future analyses. |
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