Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data

Xiao, Jingfeng; Zhuang, Qianlai; Baldocchi, Dennis; Law, B. E.; Richardson, Andrew D.; Chen, Jiquan; Oren, Ram; Starr, Gregory; Noormets, Asko; Ma, Siyan; Verma, Shashi B.; Wharton, Sonia; Wofsy, Steven C.; Bolstad, Paul V.; Burns, Sean P.; Cook, David; Curtis, Peter S.; Drake, Bert G.; Falk, Matthias; Fischer, M. L.; Foster, David R.; Gu, Lianhong; Hadley, Julian L.; Hollinger, David Y.; Katul, Gabriel G.; Litvak, M. E.; Martin, Timothy A.; Matamala, Roser; McNulty, Steven G.; Meyers, Tilden P.; Monson, Russell K.; Munger, J. William; Oechel, Walter C.; U, Kyaw Tha Paw; Schmid, Hans‐Peter; Scott, Russell L.; Sun, Ge; Suyker, Andrew E.; Torn, M. S.

doi:10.1016/j.agrformet.2008.06.015

Cited by 241 publications

(265 citation statements)

References 95 publications

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“…biophysically represents vegetation water stress using energy partitioning scheme (the more energy goes to latent heat, the less water stress for the vegetation) which already contained the effect of VPD (Yuan et al, 2007). By contrast, the fw in VPM was defined as the function of LSWI, which is sensitive to leaf water content instead of canopy water content (Maki et al, 2004), and the regional GPP is limited more by the moisture condition at canopy level (Xiao et al, 2010). Moreover, the LSWI cannot reflect the atmospheric water stress because of the weak effects of atmospheric water vapor on NIR (841-875 nm) and SWIR (1628-1652 nm) bands that were used to calculate LSWI (Gao, 1996).…”

Section: Comparisons Of the Methods To Define Water Stressmentioning

confidence: 99%

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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a b s t r a c tLight use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPD s ). We suggested that a modified VPM by using minimum method and adding VPD s may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPD s only is optimal.

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Section: Comparisons Of the Methods To Define Water Stressmentioning

confidence: 99%

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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“…It is http://dx.doi.org/10.1016/j.ecolmodel.2015.03.001 0304-3800/© 2015 Elsevier B.V. All rights reserved. difficult for satellite technology to monitor various respiratory processes, especially those in the soil (Valentini et al, 2000;Running et al, 2004;Olofsson et al, 2008;Xiao et al, 2008;Tang et al, 2012), which limit the application of remote sensing data in R e estimation. However, R e showed close relationships with easily satellite-retrieved GPP (e.g., Knohl et al, 2005;Tang et al, 2005;Moyano et al, 2007Moyano et al, , 2008Larsen et al, 2007;Bahn et al, 2008;Gomez-Casanovas et al, 2012;Huang and Niu, 2013) and temperature (e.g., Lloyd and Taylor, 1994;Frank et al, 2002;Reichstein et al, 2003;Bond-Lamberty and Thomson, 2010) in most of ecosystems, and upon these relationships some empirical or semi-empirical satellite-driven R e models were developed and validated at the plot or regional scale (Vourlitis et al, 2003;Gilmanov et al, 2005;Rahman et al, 2005;Schubert et al, 2010;Jägermeyr et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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“…So called model trees are one example of a machine learning algorithm that can be trained to predict the fluxes and have been employed for the US to predict NEE (Xiao et al, 2008). Model trees are tree shaped structures that partition the data space into units where a specific model (usually a regression) is valid.…”

Section: Introductionmentioning

confidence: 99%

“…Upscaling exercises of eddy covariance based carbon fluxes to large regions has been conducted for the US (Xiao et al, 2008, Yang et al, 2007 and Europe Papale and Valentini, 2003;Vetter et al, 2008), which are both characterized by a comparatively dense network of towers. The upscaling principle generally employs the training of a machine learning algorithm to predict carbon flux estimates based on measured meteorological data, remotely sensed vegetation properties, and vegetation type.…”

Section: Introductionmentioning

confidence: 99%

Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

2009

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Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time.We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998-2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity.We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced Correspondence to: M. Jung (mjung@bgc-jena.mpg.de) (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.

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Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data

Abstract: a g r i c u l t u r a l a n d f o r e s t m e t e o r o l o g y 1 4 8 ( 2 0 0 8 ) 1 8 2 7 -1 8 4a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j

Cited by 241 publications

References 95 publications

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Comparison of four light use efficiency models for estimating terrestrial gross primary production

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

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