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dc.contributor.authorYuan, Wenping
dc.contributor.authorLiu, Shuguang
dc.contributor.authorZhou, Guangsheng
dc.contributor.authorZhou, Guoyi
dc.contributor.authorTieszen, Larry L.
dc.contributor.authorBaldocchi, Dennis D.
dc.contributor.authorBernhofer, Christian
dc.contributor.authorGholz, Henry
dc.contributor.authorGoldstein, Allen H.
dc.contributor.authorGoulden, Michael L.
dc.contributor.authorHollinger, David Y.
dc.contributor.authorHu, Yueming
dc.contributor.authorLaw, Beverly E.
dc.contributor.authorStoy, Paul C.
dc.contributor.authorVesala, Timo
dc.contributor.authorWofsy, Steven C.
dc.date.accessioned2019-02-26T22:31:51Z
dc.date.available2019-02-26T22:31:51Z
dc.date.issued2007-04
dc.identifier.citationYuan, Wenping, Shuguang Liu, Guangsheng Zhou, Guoyi Zhou, Larry L. Tieszen, Dennis D. Baldocchi, Christian Bernhofer, Henry Gholz, Allen H. Goldstein, Michael L. Goulden, David Y. Hollinger, Yueming Hu, Beverly E. Law, Paul C. Stoy, Timo Vesala, and Steven C. Wofsy. “Deriving a Light Use Efficiency Model from Eddy Covariance Flux Data for Predicting Daily Gross Primary Production Across Biomes.” Agricultural and Forest Meteorology 143, no. 3–4 (April 2007): 189–207. doi:10.1016/j.agrformet.2006.12.001.en_US
dc.identifier.issn0168-1923
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/15299
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.rightsThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.titleDeriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomesen_US
dc.typeArticleen_US
mus.citation.extentfirstpage189en_US
mus.citation.extentlastpage207en_US
mus.citation.issue3-4en_US
mus.citation.journaltitleAgricultural and Forest Meteorologyen_US
mus.citation.volume143en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1016/j.agrformet.2006.12.001en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage12en_US


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