Leaf Optical Properties

Jacquemoud, Stéphane; Ustin, Susan L.

doi:10.1017/9781108686457

Cited by 132 publications

(163 citation statements)

References 1,632 publications

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“…The model was further tested on the LOPEX93 [55] and ANGERS [56] datasets, which showed a similar systematical bias as model results from PROSPECT spectra (Figure 5a,c,e). Since the overestimation seemed to be solely defined by the slope of the regression line, the water absorption Applied to PROSAIL spectra ( Figure 4b) the R 2 -results (0.68) are significantly lower and although model results correspond to LUT C w -values, both regression residuals and intercept do not show a systematic bias.…”

Section: Using Prospect For Calibration Of the Pwr Modelmentioning

confidence: 84%

“…The model was further tested on the LOPEX93 [55] and ANGERS [56] datasets, which showed a similar systematical bias as model results from PROSPECT spectra (Figure 5a,c,e). Since the overestimation seemed to be solely defined by the slope of the regression line, the water absorption coefficients in Equation (5) were adjusted by multiplying the slope of the PROSPECT LUT linear regression model as a constant (Equation (6)):…”

Section: Using Prospect For Calibration Of the Pwr Modelmentioning

confidence: 84%

“…In total, the database comprises 330 spectra with corresponding measurements of EWT. Secondly, tests were performed on the ANGERS database containing 276 reflectance spectra and EWT measurements of 43 species [56]. While woody species make up the majority of the ANGERS database, both datasets represent a large variety of leaf internal structure and spectra.…”

Section: Leaf Optical Datamentioning

confidence: 99%

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Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

et al. 2018

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Quantitative equivalent water thickness on canopy level (EWT canopy ) is an important land surface variable and retrieving EWT canopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R 2 ) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R 2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWT canopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWT canopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWT canopy in the scope of future hyperspectral satellite missions.

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Section: Using Prospect For Calibration Of the Pwr Modelmentioning

confidence: 84%

“…The model was further tested on the LOPEX93 [55] and ANGERS [56] datasets, which showed a similar systematical bias as model results from PROSPECT spectra (Figure 5a,c,e). Since the overestimation seemed to be solely defined by the slope of the regression line, the water absorption coefficients in Equation (5) were adjusted by multiplying the slope of the PROSPECT LUT linear regression model as a constant (Equation (6)):…”

Section: Using Prospect For Calibration Of the Pwr Modelmentioning

confidence: 84%

Section: Leaf Optical Datamentioning

confidence: 99%

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Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

et al. 2018

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“…This is characterized by yellowing and wilting of these leaves. The healthy leaves exhibit a lower transmission spectrum between 400 nm and 500 nm and at 660 nm the absorption zone of photosynthetic pigments (Chl a, Chl b and carotenoids) [36], allowing them to synthesize their organic matter necessary for their development. At the level of the reflection mode, it is observed that the control maize has reflection intensity higher than the leaves with herbextra between 500 nm and 600 nm but at 660 nm and in the near IR and IR there is an inversion of evolution of the intensities.…”

Section: Discussionmentioning

confidence: 99%

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Kouakou¹,

Soro²,

Taky³

et al. 2017

SAR

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Evaluation of the impact of herbicides on maize was done through multispectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

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“…Por outro lado, a formação de geada muito severa mata as folhas, tornando-as marrons e proporcionando uma queda mais rápida do galho (Palm Jr., 2009 A limitação das atividades funcionais das plantas ocorre por meio da senescência programada advinda de um processo evolutivo, medida econômica que assegura uma transição temporal por um período de dormência em regiões onde ocorrem períodos com condições estressantes (Larcher, 1994 A maneira como as folhas das plantas interagem com o ambiente não é ainda completamente entendida. Sabe-se que a quantidade de luz absorvida pelas plantas está atrelada à saúde da folha e a sua capacidade de absorver e metabolizar energia (Jacquemoud e Ustin, 2008). Estes autores informam que relações quantitativas entre características óticas e propriedades bioquímicas da planta (que, por sua vez, dependem de fatores externos), como as respostas ao envelhecimento das folhas ou a ambientes estressantes, elevam a refletância na faixa espectral da radiação visível, como é apresentado no Capítulo 3.0 deste trabalho.…”

Section: Fitofisionomiaunclassified

Albedo em cerrado sensu stricto como resposta à variação climática e biológica: conexões com índice de vegetação, estoques de carbono e fluxos de CO2

Couto¹

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Leaf Optical Properties

Cited by 132 publications

References 1,632 publications

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Albedo em cerrado sensu stricto como resposta à variação climática e biológica: conexões com índice de vegetação, estoques de carbono e fluxos de CO2

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