On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

Sims, Daniel A.; Rahman, Abdullah F.; Cordova, Vicente D.; Masri, Bassil El; Baldocchi, Dennis; Flanagan, Lawrence B.; Goldstein, Allen H.; Hollinger, David Y.; Misson, Laurent; Monson, Russell K.; Oechel, W. C.; Schmid, Hans‐Peter; Wofsy, Steven C.; Xu, Liukang

doi:10.1029/2006jg000162

Cited by 302 publications

(271 citation statements)

References 61 publications

(79 reference statements)

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“…EVI and MODIS GPP) for the 3 x 3 km means surrounding the tower (e.g. Sims et al 2006), thus for comparison purposes we used the 3 x 3 km mean MTCI, MODIS GPP and EVI for the rest of our analysis. Out of the 20 correlations computed between the MTCI and GPP, 15 were statistically significant, although the strength of the correlations varied between and sometimes within land cover types.…”

Section: Resultsmentioning

confidence: 99%

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

Harris

Dash

2010

Remote Sensing of Environment

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In this study we evaluated the potential of the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) for monitoring gross primary productivity (GPP) across fifteen eddy covariance towers encompassing a wide variation in North American vegetation composition. The acrosssite relationship between MTCI and tower GPP was stronger than that between either the MODIS GPP or EVI and tower GPP, suggesting that data from the MERIS can be used as a valid alternative to MODIS for estimating carbon fluxes. Correlations between tower GPP and both vegetation indices (EVI and MTCI) were similar only for deciduous vegetation, indicating that physiologically driven spectral indices, such as the MTCI, may also be able to complement existing structurally-based indices in satellite-based carbon flux modeling efforts. 1.Introduction Quantitative estimates of carbon dioxide exchange at regional to global scales are critical to improve our understanding of the links between carbon and climate. Tower-based eddy covariance (EC) techniques have been used across a wide range of ecosystems to provide information on seasonal and inter-annual carbon fluxes. However, flux tower sites only account for carbon fluxes within the designated tower footprint and the number and geographical distribution of towers across the globe is limited. Other attempts at estimating terrestrial carbon fluxes have concentrated on the development of process-based ecosystem exchange models (e.g. the Boreal Ecosystem Productivity Simulator (BEPS; Liu et al. 1997) and the Terrestrial Ecosystem Model (TEM; e.g. Raich et al. 1991)). Whilst such models show great promise, their applicability at regional and global scales is challenging due to their complexity and requirements for data that are often scarce or unavailable at the appropriate spatial and temporal scales. Carbon flux models that are driven by remotely sensed observations can be used to estimate gross primary productivity (GPP) frequently and over large areas; for example the NASA Carnegie-Ames-Stanford (NASA-CASA) model (Potter et al. 1993), the Terrestrial Uptake and Release of Carbon (TURC) model (Ruimy et al. 1996) and the Moderate Resolution Imaging Spectrometer Global Primary Productivity (MODIS-MOD17 GPP) model (Running et al. 2004)). The vast majority of satellite-based models are 'Production Efficiency Models' (PEMs) based on the light use (LUE) efficiency concept for conversion of absorbed photosynthetically active radiation (APAR) into biomass (Monteith 1977). In most PEMs the maximum LUE is empirically derived based on vegetation type and then reduced according to meteorological indicators of environmental stress. Thus whilst some PEM parameters can be estimated from satellite data, for example the fraction of absorbed photosynthetically active radiation (fPAR; Myneni et al., 2003;Prince & Goward 1995), the estimation of others, such as LUE, depend upon the availability of metrological data and vegetation maps. There can, however, be substantial errors in the...

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Section: Resultsmentioning

confidence: 99%

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

Harris

Dash

2010

Remote Sensing of Environment

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“…Detailed descriptions of these models can be found in previous publications (Gitelson et al, 2006;Sims et al, 2006Sims et al, , 2008Wu, Munger, et al, 2010;Wu, Niu, & Gao, 2010;Wu, Gonsamo, Zhang, et al, 2014;Xiao, Zhang et al, 2004).…”

Section: The Evi-based Gpp Modelsmentioning

confidence: 99%

“…The Vegetation Index (VI) model (Wu, Niu, & Gao, 2010) used EVI as proxies of both LUE and FPAR green which simplified the model structure. Several other models also used EVI to estimate GPP directly through a statistical modeling approach (Sims et al, 2008;Wu, Chen, & Huang, 2011), including the Temperature and Greenness (TG) model (Sims et al, 2006(Sims et al, , 2008 and the Greenness and Radiation (GR) model (Gitelson et al, 2006) which considered EVI as the proxies of FPAR green and FPAR chl , respectively. As these four models use EVI to estimate FPAR, they are referred as EVI-based model thereafter.…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation canopy is comprised of both photosynthetic (chlorophyll or green leaves) and non-photosynthetic components of vegetation. The other group models used the FPAR at the chlorophyll or green leaf level (FPAR chl or FPAR green ) (Gitelson et al, 2006;Sims et al, 2006;Wu, Niu, & Gao, 2010;Xiao, Zhang, et al, 2004;Zhang, Middleton, Cheng, & Landis, 2013;Zhang et al, 2006Zhang et al, , 2009 (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, no specific water stress related variables are directly considered in the TG, GR, and VI models (Gitelson et al, 2006;Sims et al, 2008;Wu, Niu, & Gao, 2010). For example, in the TG model, water stress was considered by transferring land surface temperature (LST) as an alternative approach (Sims et al, 2006(Sims et al, , 2008. The VI model uses EVI as proxy of a synthetic LUE (Wu, Munger, et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

102

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Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

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Estimating soil respiration using spatial data products: A case study in a deciduous broadleaf forest in the Midwest USA

Huang

Niu

2014

JGR Atmospheres

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This study aimed to investigate the potential of spatially distributed data products in estimating soil respiration (R s ), including land surface temperature (LST) and spectral vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) and root zone soil moisture derived from the assimilation of the NASA Advanced Microwave Scanning Radiometer-EOS and a land surface model, at a deciduous broadleaf forest site in the Midwest USA. Several statistical models were used to examine the dependencies of R s on these spatial data products, and accuracy of these models was compared to the models based on in situ measurements. The models based on mean LST (i.e., averaging nighttime and daytime LST from MODIS) and root zone soil moisture explained 82% and 72% of seasonal variations in R s for spring and winter dormant periods, respectively. In the growing season, the models depending on mean LST, root zone soil moisture, and photosynthesis-related enhanced vegetation index showed comparable accuracy with the models entirely based on in situ measured data, except for the midgrowing period. Drought stress led to a relatively low explanation capacity for the R s model based on spatial data products during the midgrowing period. However, this model still explained 76% of temporal dynamics of R s over the midgrowing period. Our results suggested that simple models based entirely on spatial data products have the potential to estimate R s at the temperate deciduous forest site. The conclusions drawn from the present study provided valuable information for large-scale estimates of R s in temperate deciduous forest ecosystems.

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On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

Cited by 302 publications

References 61 publications

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Estimating soil respiration using spatial data products: A case study in a deciduous broadleaf forest in the Midwest USA

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