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| Titel |
Towards operational remote sensing of forest carbon balance across Northern Europe |
| VerfasserIn |
P. Olofsson, F. Lagergren, A. Lindroth, J. Lindström, L. Klemedtsson, W. Kutsch, L. Eklundh |
| 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 ; 5, no. 3 ; Nr. 5, no. 3 (2008-05-19), S.817-832 |
| Datensatznummer |
250002524
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| Publikation (Nr.) |
 copernicus.org/bg-5-817-2008.pdf |
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| Zusammenfassung |
Monthly averages of ecosystem respiration (ER), gross primary
production (GPP) and net ecosystem exchange (NEE) over
Scandinavian forest sites were estimated using regression models
driven by air temperature (AT), absorbed photosynthetically active
radiation (APAR) and vegetation indices. The models were
constructed and evaluated using satellite data from Terra/MODIS
and measured data collected at seven flux tower sites in northern
Europe. Data used for model construction was excluded from the
evaluation. Relationships between ground measured variables and
the independent variables were investigated.
It was found that the enhanced vegetation index (EVI) at 250 m
resolution was highly noisy for the coniferous sites, and hence, 1
km EVI was used for the analysis. Linear relationships between EVI
and the biophysical variables were found: correlation coefficients
between EVI and GPP, NEE, and AT ranged from 0.90 to 0.79 for the
deciduous data, and from 0.85 to 0.67 for the coniferous data. Due
to saturation, there were no linear relationships between
normalized difference vegetation index (NDVI) and the ground
measured parameters found at any site. APAR correlated better with
the parameters in question than the vegetation indices. Modeled
GPP and ER were in good agreement with measured values, with more
than 90% of the variation in measured GPP and ER being explained
by the coniferous models. The site-specific respiration rate at
10°C (R10) was needed for describing the ER variation
between sites. Even though monthly NEE was modeled with less
accuracy than GPP, 61% and 75% (dec. and con., respectively) of
the variation in the measured time series was explained by the
model. These results are important for moving towards operational
remote sensing of forest carbon balance across Northern Europe. |
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