Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

By W. Yuan, Shuguang Liu, Guangsheng Zhou, Guoyi Zhou, L. Tieszen, Dennis D. Baldocchi, C. Bernhofer, H. Gholz, A. Goldstein, Michael L. Goulden and 6 more. 2007

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The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b0c5925f7ba8162b2ff100a0b4313930f6518899

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Citations in the corpus

‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, background): “Errors are generality attributed to how well stresses associated with soil moisture, humidity deficits and temperature are quantified (Xiao et al. 2004; Leuning et al. 2005; Turner et al. 2005; Zhao et al. 2005; Heinsch et al. 2006; Yuan et al. 2007).”
Vegetation-specific model parameters are not required for estimating gross primary production (2014, background): “1 ◦C (Yuan et al., 2007).”