Optimization of soil-adjusted vegetation indices

Rondeaux, G.; Steven, M. D.; Baret, Frédéric

doi:10.1016/0034-4257(95)00186-7

Cited by 2,040 publications

(708 citation statements)

References 20 publications

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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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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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“…Although not explicitly stated by the authors, both indices follow the idea of the above ratio approach for estimating LCC. Advantage of the OSAVI over other SAVI type of indices is that knowledge on soil line parameters is not required (Rondeaux, Steven, and Baret 1996). Next, Haboudane et al (2008) proposed the ratio between the triangular chlorophyll index (TCI) and the OSAVI for estimating LCC.…”

Section: Introductionmentioning

confidence: 99%

Estimating potato leaf chlorophyll content using ratio vegetation indices

Kooistra

Clevers

2016

Remote Sensing Letters

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Chlorophyll content at leaf level is an important variable because of its crucial role in photosynthesis and in understanding plant functioning. In this study, we tested the hypothesis that the ratio of a vegetation index (VI) for estimating canopy chlorophyll content (CCC) and one for estimating leaf area index (LAI) can be used to derive chlorophyll content at the leaf level. This hypothesis for estimating chlorophyll content at the leaf level was tested using simulations with the PROSAIL radiative transfer model and field spectroradiometry measurements in five consecutive years (2010)(2011)(2012)(2013)(2014) for potato crops on experimental fields. During the growing season, in-situ field measurements of LAI and leaf chlorophyll content (LCC) were performed. Results showed that good estimates of LCC were feasible using ratio vegetation indices (VIs). This was tested at satellite level using RapidEye images. This letter presents a proof of concept for estimating LCC using Sentinel-2 data. Results confirm the importance of the red-edge bands for agricultural applications, but also showed that indices using the red-edge bands may be replaced by indices using green bands. It should now be tested with real Sentinel-2 data whether its spectral bands at 10 m spatial resolution are suitable for estimating LCC, avoiding the need for red-edge bands that only are available at 20 m. ARTICLE HISTORY

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“…Índices de vegetação (IV) que apresentam maior correlação com índice de severidade de doença (ISD) para reflectâncias hiperespectral e multiespectral e resistentes aos efeitos da reflectância de solo e de matérias não fotossintéticas (Haboudane et al, 2002). OSAVI é um índice que também minimiza o efeito do solo (Rondeaux et al, 1996). Assim, a razão entre MCARI e TCARI com OSAVI combina com as respectivas habilidades de cada um, de forma a responder às variações da clorofila, minimizando os efeitos de reflectância de fundo de solo (Zarco-Tejada et al, 2004;Haboudane et al, 2002).…”

Section: Resultsunclassified

“…Foram calculados oito índices de vegetação comuns à espectrometria hiperespectral e imagem multiespectral (Tabela 1), dados por: Difference Vegetation Index-DVI (Tucker, 1979); Green Normalized Difference Vegetation Index-GNDVI (multiespectral: Gitelson et al, 1996;hiperespectral: Daughtry et al, 2000); Modified Chlorophyll Absorption in Reflectance Index-MCARI (Daughtry et al, 2000); Modified Soil Adjusted Vegetation Index-MSAVI (Qi et al, 1994); Normalized Difference Vegetation Index-NDVI (Rouse et al, 1973); Optimized Soil Adjusted Vegetation Index-OSAVI (Rondeaux et al, 1996); Relative Difference Vegetation Index-RDVI (Roujean & Breon, 1995); Transformed Chlorophyll Absorption Reflectance Index-TCARI (Haboudane et al, 2002). Foram utilizadas, também, as razões MCARI/OSAVI (Zarco-Tejada et al, 2004) e MCARI/ OSAVI (Haboudane et al, 2002).…”

Section: Methodsmentioning

confidence: 99%

Estimativa de severidade do mofo-branco em lavouras de feijão utilizando-se sensores hiper e multiespectral

Machado

Pinto

Queiroz

et al. 2015

Rev. bras. eng. agríc. ambient.

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R E S U M ONeste estudo objetivou-se identificar comprimentos de onda e faixas espectrais provenientes de reflectâncias hiper e multiespectrais utilizando regressão PLS e promover avaliação comparativa desses métodos e de dez índices de vegetação, para determinar aqueles que melhor estimam níveis de severidade de mofo-branco em feijão. Foram implantados experimentos nos municípios de Viçosa e de Oratórios, estado de Minas Gerais. Reflectâncias hiperespectrais foram obtidas com espectroradiômetro cuja faixa útil de leitura adotada foi entre 440 e 900 nm. Reflectâncias multiespectrais foram obtidas de imagens de câmara constituídas de cinco bandas (vermelho, verde, azul, Red-edge e infravermelho). Os índices de severidade da doença foram baixos; em Viçosa a média foi de 5,8% e em Oratórios, 7,4%. Modelos matemáticos utilizando reflectâncias hiperespectrais tiveram melhor desempenho para estimar mofo-branco; a banda do red-edge apresentou os comprimentos de onda que melhor estimam a severidade do mofo-branco. Índices de vegetação resistentes a efeitos da reflectância de solo estimaram melhor o mofo-branco do que os demais índices.Estimative of white mold severity in common bean crops using hyper and multispectral sensors A B S T R A C TThis study aimed to identify wavelengths and spectral ranges from hyper and multispectral reflectance using PLS regression; and to promote comparative evaluation of these methods and ten vegetation indices to determine those that best estimate levels of white mold severity in common beans. Experiments were implemented in the municipalities of Viçosa and Oratorios, Minas Gerais state. Hyperspectral reflectance measurements were acquired with the spectroradiometer, whose useful reading range was between 440 and 900 nm. Multispectral reflectance measurements were obtained from camera images comprising five bands (red, green, blue, red-edge and infrared). The indexes of disease severity were low. In Viçosa the average was 5.8% and in Oratorios, 7.4%. Mathematical models using hyperspectral reflectance performed better for estimating white mold. The red-edge band presented the wavelengths that best estimate the severity of white mold. Vegetation index resistant to the soil reflectance effects was better to estimate white mold than the other indices. Palavras-chave:regressão PLS índice de vegetação agricultura de precisão

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Optimization of soil-adjusted vegetation indices

Cited by 2,040 publications

References 20 publications

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

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

Estimating potato leaf chlorophyll content using ratio vegetation indices

Estimativa de severidade do mofo-branco em lavouras de feijão utilizando-se sensores hiper e multiespectral

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