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High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992-2008

Publiceringsår: 2012
Språk: Engelska
Sidor: 407-416
Publikation/Tidskrift/Serie: ITC Journal
Volym: 18
Dokumenttyp: Artikel i tidskrift
Förlag: Elsevier


Arctic ecosystems play a key role in the terrestrial carbon cycle. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with field measurements of CO2 fluxes to investigate changes in gross primary production (GPP) for the peak growing seasons 1992-2008 in Rylekaerene, a wet tundra ecosystem in the Zackenberg valley, north-eastern Greenland. A method to incorporate controls on GPP through satellite data is the light use efficiency (LUE) model, here expressed as GPP = epsilon(peak) x PAR(in) x FAPAR(green_peak); where epsilon(peak) was peak growing season light use efficiency of the vegetation, PARin was incoming photosynthetically active radiation, and FAPAR(green_peak) was peak growing season fraction of PAR absorbed by the green vegetation. The Speak was measured for seven different high-Arctic plant communities in the field, and it was on average 1.63 g CO2 MJ(-1). We found a significant linear relationship between FAPARgreen_peak measured in the field and satellite-based NDVI. The linear regression was applied to peak growing season NDVI 1992-2008 and derived FAPAR(green_peak) was entered into the LUE-model. It was shown that when several empirical models are combined, propagation errors are introduced, which results in considerable model uncertainties. The LUE-model was evaluated against field-measured GPP and the model captured field-measured GPP well (RMSE was 192 mg CO2 m(-2) h(-1)). The model showed an increase in peak growing season GPP of 42 mg CO2 m(-2) h(-1) y(-1) in Rylekaerene 1992-2008. There was also a strong increase in air temperature (0.15 degrees C y(-1)), indicating that the GPP trend may have been climate driven. (C) 2012 Elsevier B.V. All rights reserved.


  • Physical Geography
  • Light use efficiency
  • NDVI
  • Remote sensing
  • Climate change
  • GPP


  • BECC
  • ISSN: 0303-2434

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