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Journal Article

Spatial analysis of growing season length control over net ecosystem exchange

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons62356

Churkina,  G.
Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Churkina, G., Schimel, D., Braswell, B. H., & Xiao, X. M. (2005). Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biology, 11(10), 1777-1787.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-D29D-E
Abstract
Using data from 28 flux measurement sites, we performed an analysis of the relationship between annual net ecosystem exchange (NEE) and the length of the carbon uptake period (CUP) (the number of days when the ecosystem is a net carbon sink). The observations suggest a linear correlation between the two quantities. The change in annual carbon exchange per day of the CUP differs significantly between deciduous and evergreen vegetation types. The sites containing vegetation with short-lived foliage (less than 1 year) have higher carbon uptake and respiration rates than evergreen vegetation. The ratio between mean daily carbon exchange rates during carbon uptake and release periods is relatively invariant (2.73 ± 1.08) across different vegetation types. This implies that a balance between carbon release and uptake periods exists despite different photosynthetic pathways, life forms, and leaf habits. The mean daily carbon sequestration rate for these ecosystems never exceeds the carbon emission rate by more than a factor of 3. Growing season lengths for the study sites were derived from the normalized difference vegetation index (NDVI) of advanced very-high-resolution radiometer and from the enhanced vegetation index (EVI) of VEGETATION SPOT-4. NDVI and EVI were found to be closely related to the CUP, and consequently they also can be used to approximate annual carbon exchange of the ecosystems. This approach has potential for allowing extrapolation of NEE over large areas from remotely sensed data, given a certain amount of ancillary information. This method could complement the currently existing techniques for extrapolation, which rely upon modeling of the individual gross fluxes. [References: 59]