Optical Properties of Vegetation Canopies

Guyot, G.

doi:10.1016/b978-0-408-04767-8.50007-4

Cited by 109 publications

(82 citation statements)

References 53 publications

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“…Their strong relationship with crop water status is potentially due to the fact that water stress manifests as an increase in green reflectance as discussed by Zygielbaum et al [67]. Nevertheless, other morphological responses (e.g., leaf angle distribution, canopy geometry) as well as phenological and physiological adaptive mechanisms to stress conditions can occur and have varied effects among species [68][69][70], which may introduce some confounding effects on the response of these structural-oriented VIs. Additionally, factors like leaf age can also influence the spectral response thus impacting these VIs responses [70].…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, other morphological responses (e.g., leaf angle distribution, canopy geometry) as well as phenological and physiological adaptive mechanisms to stress conditions can occur and have varied effects among species [68][69][70], which may introduce some confounding effects on the response of these structural-oriented VIs. Additionally, factors like leaf age can also influence the spectral response thus impacting these VIs responses [70]. Contrarily, PRI is a stress/physiology-oriented hyperspectral VI [49] designed for measuring subtle decreases of reflectance around 531 nm due to changes in the xanthophyll cycle pigment activity resulting from stress conditions, including water stress conditions [32,34,68].…”

Section: Discussionmentioning

confidence: 99%

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Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r 2 ) smaller than 0.67. However, the results of r 2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r 2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R 2 = 0.79 and a regression coefficient b = 0.96. Similar R 2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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“…Most of these studies have shown that canopy reflectance within the red (600-710 nm) and near infra-red (750-900 nm) wavelengths were correlated with disease severity estimates. In the visible range reflectance is considered to be influenced by leaf pigments such as chlorophyll and in the near-infrared range reflectance is affected by changes in the anatomical structure of leaves [40,41].…”

Section: Discussionmentioning

confidence: 99%

Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

2006

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Identification of powdery mildew (Erysiphe graminis sp. tritici ) and take-all disease (Gaeumannomyces graminis sp. tritici ) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements Abstract: The ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380 -1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490 780 nm (r 2 = 0.69), 510 780 nm (r 2 = 0.74), 516 1300 nm (r 2 = 0.62) and 540 1300 nm (r 2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490 780 nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

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“…Variation may arise from the environmental growth conditions, varying anatomy, morphology and pigmentation and diverse species differences. Such factors can dramatically influence the visible reflectance, and PRI (Guyot, 1990).…”

Section: Canopy Pri and Photosynthetic Luementioning

confidence: 99%

Remote sensing of photosynthetic-light-use efficiency of boreal forest

Nichol

Huemmrich

Black

et al. 2000

Agricultural and Forest Meteorology

178

128

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Using a helicopter-mounted portable spectroradiometer and continuous eddy covariance data we were able to evaluate the photochemical reflectance index (PRI) as an indicator of canopy photosynthetic light-use efficiency (LUE) in four boreal forest species during the Boreal Ecosystem Atmosphere experiment (BOREAS). PRI was calculated from narrow waveband reflectance data and correlated with LUE calculated from eddy covariance data. Significant linear correlations were found between PRI and LUE when the four species were grouped together and when divided into functional type: coniferous and deciduous. Data from the helicopter-mounted spectroradiometer were then averaged to represent data generated by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We calculated PRI from these data and relationships with canopy LUE were investigated. The relationship between PRI and LUE was weakened for deciduous species but strengthened for the coniferous species. The robust nature of this relationship suggests that relative photosynthetic rates may be derived from remotely-sensed reflectance measurements.

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Optical Properties of Vegetation Canopies

Cited by 109 publications

References 53 publications

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

Remote sensing of photosynthetic-light-use efficiency of boreal forest

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