Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Du, Lina; Gong, Wei; Shi, Shuo; Yang, Jian; Sun, Jiayue; Zhu, Bin; Song, Shalei

doi:10.1016/j.jag.2015.08.008

Cited by 95 publications

(72 citation statements)

References 56 publications

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“…Among them, 32 reflectance intensity values measured by a 32-channel HSL system [34] and four LIF peaks found in the fluorescence spectrum were included [37]. Several variables of these 32 reflectance intensities might be poorly related to LNC and could thus be removed without decreasing the regression accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Reflectance spectra of rice have been collected in the laboratory by a HSL system which has been introduced in detail in the study of Du et al [34]. The laser emission component of HSL is a wide-band supercontinuum laser with a frequency of 20-40 kHz and a pulse duration of 1-2 ns.…”

Section: Reflectance Spectrum Of Rice Leafmentioning

confidence: 99%

“…In the study of Du et al [34], SVMs were adopted to retrieve rice LNC, and they found that the Whether in booting or heading stages, SVM shows great potential in rice LNC estimation, whose maximum R 2 can be more than 0.9 (0.963 in the booting stage with RMSE = 0.093 and 0.932 in the heading stage with RMSE = 0.1). The RMSE can be calculated by Equation (12), where X observed,i is the measured LNC, X modeled,i is the retrieved LNC, and N is the number of samples.…”

Section: Lnc Estimation Using Reflectance Spectra Based On Different mentioning

confidence: 99%

“…In the study of Du et al [34], SVMs were adopted to retrieve rice LNC, and they found that the R 2 values of LNC retrieval models could become greater than 0.7 when all wavelengths were used as the input variables. In this study, all wavelengths were ranked on the basis of the corresponding feature weights before the LNC estimation.…”

Section: Lnc Estimation Using Reflectance Spectra Based On Different mentioning

confidence: 99%

“…In some bands of this spectral range, the light is strongly absorbed by chlorophyll in green plants, which is a sensitive indicator of the LNC. More details about the bands' range and their corresponding channels can be found in the study of Du et al [34]. …”

Section: Band Combinations Based On Hsl Data For Rice Lnc Estimationmentioning

confidence: 99%

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Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Reflectance Spectrum Of Rice Leafmentioning

confidence: 99%

Section: Lnc Estimation Using Reflectance Spectra Based On Different mentioning

confidence: 99%

Section: Lnc Estimation Using Reflectance Spectra Based On Different mentioning

confidence: 99%

Section: Band Combinations Based On Hsl Data For Rice Lnc Estimationmentioning

confidence: 99%

See 3 more Smart Citations

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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The application of time decay characteristics of laser‐induced fluorescence in the classification of vegetation

et al. 2016

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In this study, the time decay of the chlorophyll fluorescence intensity (TDCFI) of vegetation was measured based on laser-induced fluorescence (LIF) technology with a 355 nm laser serving as the excitation light source. The pseudo-color diagram of the TDCFI (PDTDCFIs) was proposed for use as a characteristic fingerprint for the analysis of various plant species based on variations in the fluorescence intensity over time. Compared with the steady-state fluorescence spectra, two-dimensional PDTDCFIs contained more spectral information, including variations in both the shape of the laser-induced fluorescence spectra and the relative intensity. The experimental results demonstrated that the PDTDCFIs of various plant species show distinct differences, and this was successfully applied in the classification of plant species. Therefore, the PDTDCFIs of plants could provide researchers with a more reliable and useful tool for the characterization of vegetation. Copyright © 2016 John Wiley & Sons, Ltd.

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