Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model

Veroustraete, Frank; Patyn, Johan; Myneni, Ranga B.

doi:10.1016/0034-4257(95)00258-8

Cited by 73 publications

(48 citation statements)

References 18 publications

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“…Compared with traditional ground-based methods, such as visual examination and survey sampling, remote sensing that provides synoptic and repetitive observations of the land surface is well suited for agricultural mapping [4][5][6] and monitoring [7,8] large geographic areas. In particular, satellite-derived vegetation indices, as measures of plant chlorophyll abundance and vegetation radiation absorption [9], have proven to be closely related to crop growth in field studies and theoretical models [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

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Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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“…Veroustraete et al (1996) estimated NEE for a deciduous forest in Belgium using a physiological process model driven by the fractional absorption of PAR (FA-PAR) from satellite and meteorological data. Chiesi et al (2005) estimated monthly NEE for a forested site in Italy by parameterizing and calibrating FOREST-BGC (Running and Coughlan, 1998), including FAPAR derived from Landsat normalized difference vegetation index (NDVI).…”

Section: Introductionmentioning

confidence: 99%

Towards operational remote sensing of forest carbon balance across Northern Europe

et al. 2008

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Abstract.Monthly averages of ecosystem respiration (ER), gross primary production (GPP) and net ecosystem exchange (NEE) over Scandinavian forest sites were estimated using regression models driven by air temperature (AT), absorbed photosynthetically active radiation (APAR) and vegetation indices. The models were constructed and evaluated using satellite data from Terra/MODIS and measured data collected at seven flux tower sites in northern Europe. Data used for model construction was excluded from the evaluation. Relationships between ground measured variables and the independent variables were investigated.It was found that the enhanced vegetation index (EVI) at 250 m resolution was highly noisy for the coniferous sites, and hence, 1 km EVI was used for the analysis. Linear relationships between EVI and the biophysical variables were found: correlation coefficients between EVI and GPP, NEE, and AT ranged from 0.90 to 0.79 for the deciduous data, and from 0.85 to 0.67 for the coniferous data. Due to saturation, there were no linear relationships between normalized difference vegetation index (NDVI) and the ground measured parameters found at any site. APAR correlated better with the parameters in question than the vegetation indices. Modeled GPP and ER were in good agreement with measured values, with more than 90% of the variation in measured GPP and ER being explained by the coniferous models. The site-specific respiration rate at 10 • C (R 10 ) was needed Correspondence to: P. Olofsson (olofsson@bu.edu) for describing the ER variation between sites. Even though monthly NEE was modeled with less accuracy than GPP, 61% and 75% (dec. and con., respectively) of the variation in the measured time series was explained by the model. These results are important for moving towards operational remote sensing of forest carbon balance across Northern Europe.

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“…Numerous studies have been conducted on the dependence of NDVI on different parameters of the vegetation cover, including CO 2 fluxes exchanged between the vegetation cover and the atmosphere. Emphasis has been particularly placed on its relationship with GEP (Rossini et al, 2012;Sakowska et al, 2014;Skinner et al, 2011;Wang et al, 2004) and NEP (Hassan et al, 2006;Propastin and Kappas, 2009;Veroustraete et al, 1996). The seasonal variation of CO 2 fluxes exchanged between different ecosystems and the atmosphere was successfully assessed based on different VIs including NDVI (Rossini et al, 2012;Wang et al, 2004;Zhang et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Uździcka¹,

Stróżecki²,

Urbaniak³

et al. 2017

International Agrophysics

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'Assessment of a seasonal and spatial variation of plant biophysical and spectral indices in the context of CO 2 , CH 4 , H 2 O gas exchange between wetland, forest and arable ecosystems and the atmosphere', 2010-2013.A b s t r a c t. The aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO 2 exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO 2 fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R 2 for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R 2 values indicate the utility of spectral vegetation indices in estimating CO 2 fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

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Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model

Cited by 73 publications

References 18 publications

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Towards operational remote sensing of forest carbon balance across Northern Europe

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

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