Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

Zhao, Feng; Huang, Yanbo; Guo, Yiqing; Reddy, Krishna N.; Lee, Matthew A.; Fletcher, Reginald S.; Thomson, Steven J.

doi:10.3390/rs6021538

Cited by 51 publications

(17 citation statements)

References 42 publications

(50 reference statements)

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“…All simulated fluorescence spectra combined with the corresponding selected values of input parameters were used as input data for the sensitivity analysis. Detailed procedure and formulae can be found in our previous studies [21,22].…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…In the first step, the leaf structure parameter N was firstly inverted from the measured data of leaf reflectance (ρleaf) and transmittance (τleaf) by minimizing the following merit function Fn(N), which is defined in the near-infrared wavelengths (λ) of 750-1300 where N is the most sensitive parameter among the input parameters of the PROSPECT-5 model [22] …”

Section: Inversion Proceduresmentioning

confidence: 99%

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Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

et al. 2015

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Abstract:In this study, backward and forward fluorescence radiance within the emission spectrum of 640-850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the OPEN ACCESSRemote Sens. 2015, 7 14180 fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R 2 ) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10 −6 W· m −2 · sr −1 · nm −1 , respectively, for backward fluorescence, and 0.92 and 3.84 × 10 −6 W· m −2 · sr −1 · nm −1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.

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Section: Sensitivity Analysismentioning

confidence: 99%

Section: Inversion Proceduresmentioning

confidence: 99%

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

et al. 2015

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“…Leaf apparent reflectance spectra were acquired by using an ASD integrating sphere apparatus coupled with an ASD FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Boulder, CO., USA), according to the procedure described by Zhao et al [5,21]. Four ChlF-related spectral indices were calculated from the leaf apparent reflectance spectra.…”

Section: Calculation Of Chlf-related Spectral Indicesmentioning

confidence: 99%

“…Many studies have been reported which analyzed the effectiveness of employing reflectance-based spectral indices and transformed spectral features in detecting glyphosate injury [5]. However, these methods have suffered from two main drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Detection of the onset of crop stress induced by glyphosate using chlorophyll fluorescence measurements

Guo

Zhao

Yan-hua

et al. 2014

2014 the Third International Conference on Agro-Geoinformatics

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In this study, Chlorophyll Fluorescence (ChlF) was used to assess and detect the onset of crop injury from glyphosate, the most widely used herbicide. Thirty-six pots of non-glyphosate-resistant soybean (cultivar FM955LL) were randomly divided into three groups and treated with different doses of glyphosate solutions. The three treatment groups were CTRL group (with no glyphosate treatment), 0.25X group (treated with 0.217 kg·ae/ha solution of glyphosate), and 0.5X group (treated with 0.433 kg·ae/ha solution of glyphosate). The Kautsky effect parameters were acquired in the experiment by using a chlorophyll fluorimeter. Reflectance-based indices, which were potentially related to ChlF, were also calculated from leaf reflectance spectra acquired using an integrating sphere apparatus coupled with a spectroradiometer. These data were processed and analyzed to study their response to the glyphosate treatments. Results indicate that four Kautsky effect parameters, Fv, Fv/Fm, Area, and PI, are parameters sensitive to glyphosate treatment, showing significant differences among the three groups at and beyond 48 Hours After Treatment (HAT). Moreover, ChlF-related spectral indices, R 675 ·R 690 /R 683 2 and R 690 /R 655 , are shown to be useful in detection of the glyphosate injury, though they are shown less effective compared with the Kautsky effect parameters. Results presented in this paper demonstrate that ChlF is effective in detecting the onset of glyphosate-induced leaf injury, and has the potential to be further developed into practical use.

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“…The SCOPE model includes leaf, canopy, soil, and meteorology variables as inputs. By using the method of global sensitivity analysis given by Zhao et al [22], the sensitivity indices of the input parameters of the SCOPE model (Version 1.51) were calculated (not shown). Results revealed that, among all the 58 input parameters, 15 of them have considerable impact on the simulated Fs spectra.…”

Section: Simulated Datamentioning

confidence: 99%

A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements

et al. 2014

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A method for canopy Fluorescence Spectrum Reconstruction (FSR) is proposed in this study, which can be used to retrieve the solar-induced canopy fluorescence spectrum over the whole chlorophyll fluorescence emission region from 640-850 nm. Firstly, the radiance of the solar-induced chlorophyll fluorescence (Fs) at five absorption lines of the solar spectrum was retrieved by a Spectral Fitting Method (SFM). The Singular Vector Decomposition (SVD) technique was then used to extract three basis spectra from a training dataset simulated by the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes). Finally, these basis spectra were linearly combined to reconstruct the Fs spectrum, and the coefficients of them were determined by Weighted Linear Least Squares (WLLS) fitting with the five retrieved Fs values. Results for simulated datasets indicate that the FSR method could accurately reconstruct the Fs spectra from hyperspectral measurements acquired by instruments of high Spectral Resolution (SR) and Signal to Noise Ratio (SNR). The FSR method was also applied to an experimental dataset acquired in a diurnal experiment. The diurnal change of the reconstructed Fs spectra shows that the Fs OPEN ACCESSRemote Sens. 2014, 6 10172 radiance around noon was higher than that in the morning and afternoon, which is consistent with former studies. Finally, the potential and limitations of this method are discussed.

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Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

Cited by 51 publications

References 42 publications

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Detection of the onset of crop stress induced by glyphosate using chlorophyll fluorescence measurements

A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements

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