Monitoring wheat canopies with a high spectral resolution radiometer

Baret, Frédéric; Champion, Isabelle; Guyot, G.; Podaire, A.

doi:10.1016/0034-4257(87)90089-7

Cited by 59 publications

(25 citation statements)

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“…HATFIELD (1981) também observou os mais altos coeficientes de correlação e a maior variabilidade na resposta espectral de genótipos de trigo, no estádio de enchimento de grão. Outro aspecto favorável na escolha dos estádios de florescimento e do início de enchimento de grãos é que o dossel das plantas está bem desenvolvido e a contribuição dos efeitos do solo, na reflectância, é mínima; além disso, as folhas superiores ainda não amareleceram (BARET et al, 1987).…”

Section: Resultsunclassified

Variação espectral e eficiência de uso da radiação fotossinteticamente ativa em ensaio com genótipos de trigo

et al. 2005

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Medidas de radiometria de campo são utilizadas para quantificar a variação espectral e a eficiência de uso da radiação solar na produção de fitomassa (épsilonF), em um experimento com 20 genótipos de trigo, cultivado na área experimental da Universidade de Taubaté, Taubaté, SP, durante o período de maio a outubro de 1999. A cultura foi irrigada em intervalos semanais, visando manter o solo próximo à capacidade de campo. Foram obtidas oito medidas do fator de refletância da cultura do trigo, entre a emergência das plântulas e a maturação fisiológica dos grãos com o radiômetro Spectron SE-590. Essas medidas foram transformadas no índice de vegetação com diferença normalizada (IVDN) e, juntamente com dados de radiação fotossinteticamente ativa (RFA), foram utilizadas no cálculo da RFA absorvida acumulada (RFAAA) ao longo do ciclo da cultura, a fim de estimar os valores de épsilonF. Pelo resultado, em eF observou-se significativa variação entre genótipos, a técnica de radiometria de campo é uma ferramenta promissora na identificação de genótipos com maior épsilonF, e ainda, para produção de fitomassa, o aproveitamento da radiação, no processo fotossintético, é bem mais relevante do que a RFAAA.

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Section: Resultsunclassified

Variação espectral e eficiência de uso da radiação fotossinteticamente ativa em ensaio com genótipos de trigo

et al. 2005

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“…These studies have shown narrow-band data to provide additional information over broadband data, enabling significant improvements in quantifying biophysical and biochemical variables of agricultural crops. A number of investigators have studied the relationship between canopy hyperspectral reflectance and canopy properties for major crops [15][16][17]. Hyperspectral studies have been successfully used in assessments of rice yield [13] and chlorophyll content of plants [6,7].…”

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Estimating biophysical parameters of rice with remote sensing data using support vector machines

Yang

Huang

Wu³

et al. 2011

Sci. China Life Sci.

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Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m 2 green leaf area m −2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m −2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m −2 . The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. The assessment of biophysical vegetation properties, such as leaf area index (LAI) and green leaf chlorophyll density (GLCD), is a major goal of remote sensing in agriculture. Remote-sensing-based assessments of these variables are made possible as a result of the strong contrast between spectral reflectances of vegetation and the soil background and the dramatic reflectance changes associated with changing vegetative cover. Based on this contrast, numerous vegetation indices (VIs) have been developed during the past few decades, which are highly correlated with the amount of vegetation. The most common of these indices use the red and near-infrared (NIR) canopy reflectances in the form of ratios, such as the ratio VI [1] and the normalized difference vegetation index [2], and as linear combinations of red and NIR reflectances [3,4]. These indices generally use averaged spectral information over broad bandwidths [5], resulting in the loss of critical information available in specific narrow bands [6], and potentially limiting the accurate estimates of agricultural crop and natural vegetation biophysical and biochemical variables [7,8]. In addition, many of these vegetation indices are strongly influenced by the soil background, resulting in soil-dependent VI-biophysical relationships [9,10]. Further improvements in quantifying vegetation are possible using spectral data from distinct narrow bands, as indicated by numerous hyperspectral studies using field spec-

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“…This method is simplest one first developed by Baret (1987) also used by Guyot (1992). It assumes that the reflectance red edge can be converted into straight line.…”

Section: B Linear Interpolation Methodsmentioning

confidence: 99%

Stress Monitoring of Mulberry Plants by Finding Rep Using Hyperspectral Data

Bhosle¹,

Musande²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Stressed on crop can be monitored using different indices. Red Edge position is good estimator for stress monitoring. The red edge position (REP) is strongly correlated with foliar chlorophyll content. Strong chlorophyll absorption causes the abrupt change in the 680-800nm region of reflectance spectra of green vegetation. The red edge consist the point of maximum slope between red and nearinfrared wavelengths. REP can be used to recognize green zone of the observation area. The REP is present in spectra for vegetation recorded by remote sensing methods. REP is clearer and significant in hyper spectral data as hyper spectral consist of more and continuous bands data. In this paper experiments were carried out for mulberry crop using USGS EO-1 Hyperion data. Atmospheric corrected data is used for classification. Classification is carried out on small cluster of 14 field samples. Ground truth is verified and classified by comparing with Hyperion data. REP is different for different stressed condition of crops and shows healthy and diseased crop condition. Nutritional stresses, diseases, drought of plants are detected using REP. Stressed and healthy field of mulberry are estimated by calculating REP using maximum first derivative, linear interpolation, linear extrapolation method. Finally REP is compared using above methods. It is noticeable difference of REP for healthy and stressed crop. The research indicates that overall accuracy using maximum first derivative was 92.85 % and it was more compared to other methods. Linear extrapolation gives less accuracy compared to linear interpolation method.

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Monitoring wheat canopies with a high spectral resolution radiometer

Cited by 59 publications

References 13 publications

Variação espectral e eficiência de uso da radiação fotossinteticamente ativa em ensaio com genótipos de trigo

Variação espectral e eficiência de uso da radiação fotossinteticamente ativa em ensaio com genótipos de trigo

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Stress Monitoring of Mulberry Plants by Finding Rep Using Hyperspectral Data

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