2010
DOI: 10.1016/j.rse.2010.01.022
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Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

Abstract: The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, 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. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP bec… Show more

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Cited by 497 publications
(368 citation statements)
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References 94 publications
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“…phenology, LAI) and function (e.g. ET, gross primary productivity) has become increasingly sophisticated (Glenn et al, 2010;Yuan et al, 2010;Jung et al, 2011;Rossini et al, 2012;Kanniah et al, 2013;Ma et al, 2013;Nagler et al, 2013) and increasingly applied to realworld applications of water resources management (Scott et al, 2008;Glenn et al, 2010;Barron et al, 2014;Doody et al, 2014). Remote sensing (RS) provides a robust and spatially explicit means to assess not only vegetation structure and function but also relationships amongst these and climate variables.…”
Section: Satellite-based Approachesmentioning
confidence: 99%
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“…phenology, LAI) and function (e.g. ET, gross primary productivity) has become increasingly sophisticated (Glenn et al, 2010;Yuan et al, 2010;Jung et al, 2011;Rossini et al, 2012;Kanniah et al, 2013;Ma et al, 2013;Nagler et al, 2013) and increasingly applied to realworld applications of water resources management (Scott et al, 2008;Glenn et al, 2010;Barron et al, 2014;Doody et al, 2014). Remote sensing (RS) provides a robust and spatially explicit means to assess not only vegetation structure and function but also relationships amongst these and climate variables.…”
Section: Satellite-based Approachesmentioning
confidence: 99%
“…Direct evidence that vegetation is using groundwater can be obtained by comparing the stable isotope composition of groundwater, soil water, surface water (if relevant) and xylem water (Thorburn et al, 1993;Zencich et al, 2002;Lamontagne et al, 2005;O'Grady et al, 2006a, b;Kray et al, 2012;Busch et al, 1992;Ehleringer and Dawson, 1992;. This method is very effective in semi-arid regions where groundwater is derived from snowmelt or winter precipitation (which is isotopically lighter than summer precipitation) (Ehleringer and Dawson, 1992;Jobbagy et al, 2011).…”
Section: Stable Isotope Analysismentioning
confidence: 99%
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“…In recent years, several studies have quantified the spatial patterns of AET at regional (e.g., Li et al, 2014;Liu et al, 2013a;Wang et al, 2013) and global scales (e.g., Mu et al, 2011;Yan et al, 2012;Yuan et al, 2010) using process-based or remote sensing models. Although the spatial patterns described by different models generally agreed well with each other for a given region (Mueller et al, 2011;Liu et al, 2013a), large obvious uncertainties in AET values still remain.…”
Section: Introductionmentioning
confidence: 99%
“…At the same time, a series of important ecological phenomena and their controling mechanism were illustrated. For example, the 'light depress' at ecosystem scale in temperate and alpine grasslands (Fu et al, 2006(Fu et al, , 2009, the nonlinear response of ecosystem carbon flux to temperature variation , heterogeneous response of soil respiration to temperature Jia et al, 2013;Song et al, 2013), the spatio-temporal variation of ecosystem light use efficiency (Yuan et al, 2010;Zhang et al, 2006c;Wu et al, 2008), and the 'carbon pool' in mountain area (Yao et al, 2012). Such studies enhance the understanding of the biotic and abiotic controlling mechanism of ecosystem CO 2 flux across different temporal scales, and the response and adaptation of ecosystem flux to global change.…”
Section: Environmental Responses Of Ecosystem Co 2 Fluxmentioning
confidence: 99%