A comparison of vegetation indices over a global set of TM images for EOS-MODIS

Huete, Alfredo; Liu, H.Q.; Batchily, Karim; Leeuwen, Willem J. D. van

doi:10.1016/s0034-4257(96)00112-5

Cited by 1,525 publications

(805 citation statements)

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“…ou.edu/visualization/). We calculated three vegetation indices, including EVI (Huete, Liu, Batchily, & vanLeeuwen, 1997;Huete et al, 2002), NDVI (Tucker, 1979), and LSWI (Xiao, Zhang, et al, 2004;Xiao et al, 2005), using the reflectance data as shown below:…”

Section: Modis Surface Reflectance Vegetation Index and Land Surfacementioning

confidence: 99%

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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Section: Modis Surface Reflectance Vegetation Index and Land Surfacementioning

confidence: 99%

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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“…2018, 10, 771 2 of 25 -a photochemical reflectance index (PRI), which shows changes in the xanthophyll cycle [26]; -a normalized difference vegetation index (NDVI) [27], an optimized soil-adjusted vegetation index (OSAVI) [28], and an enhanced vegetation index (EVI) [29,30], which quantitatively show a photosynthesizing biomass; -a chlorophyll index (CI), which shows chlorophyll content in leaves [31,32]; and -a structural independent pigment index (SIPI), which is connected to the ratio of carotenoids to chlorophylls [33].…”

Section: Introductionmentioning

confidence: 99%

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

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Abstract:The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters-including quantum yield of photosystem II (∆F/Fm'), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)-were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ∆F/Fm' and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.

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“…A estreita correlação entre dinâmica espaço-temporal da vegetação e índices de vegetação mostra o seu potencial para o monitoramento da cobertura vegetal sobre áreas extensas (Risso et al, 2012). Entre esses índices, destacam-se o "normalized difference vegetation index" (NDVI), baseado na refletância do vermelho e do infravermelho (Rouse et al, 1974); e o "enhanced vegetation index" (EVI), que inclui a refletância do azul (Huete et al, 1997).…”

Section: Introductionunclassified

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

Wagner

Fontana

Fraisse³

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R 2 >0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo.Termos para indexação: campos nativos, EVI, Modis, NDVI, séries temporais. Temporal trends of vegetation indices on Pampa grasslands in Brazil and UruguayAbstract -The objective of this work was to evaluate the reduction in the vegetative vigor of Pampa vegetation cover in Brazil and Uruguay, by identifying negative trends in images time series. The following were used: time series of NDVI/EVI images from the Modis sensor, from 2000 to 2011; images from soil moisture indices from the climate forecast system reanalysis; and precipitation data from meteorological stations. The study quantified linear and nonlinear trends in the NDVI and EVI series in grassland areas. With the MannKendall monotonic trend, at 5% probability, 81.9% of the total area studied was significant with NDVI, and 74.8% with EVI; however, EVI showed superior contrast in the estimation of parameters. The results showed: highest negative signal in the west, with average values of R 2 >0.15, r<-0.3, and τ <-0.15 in the tendency of the vegetation indices; and decreasing tendency for NDVI, EVI, and rainfall, with lower mean soil moisture values. The negative trend of the vegetation indices, related to the combination of drought occurrence in surface soils with excessive grazing, indicates changes in the pattern of Pampa vegetation cover, with reduction in vegetative vigor.

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A comparison of vegetation indices over a global set of TM images for EOS-MODIS

Cited by 1,525 publications

References 33 publications

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

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

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

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