Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data

He, Jue; Zhang, Xiangbin; Guo, Wanting; Pan, Yuanyuan; Yao, Xia; Cheng, Tao; Zhu, Yan; Cao, Weixing; Tian, Yongchao

doi:10.3389/fpls.2019.01802

Cited by 29 publications

(19 citation statements)

References 46 publications

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“…Due to the high reflectivity of green plants due to the strong absorption of chlorophyll in the red light band and the multiple scattering in the mesophyll cell structure and canopy in the near-infrared band, they are often used for multiple combinations such as ratio, difference, and linear combination, forming a clear contrast to enhance or reveal the hidden plant information. However, despite the extensive research performed on crops such as wheat (Triticum aestivum L.), studies on the remote sensing monitoring of the vertical distribution of chlorophyll content in maize is lacking, perhaps due to the difficulties of this application [4][5][6][7][8][9]. Soil and plant analyzer development (SPAD) is one of the most important indicators for the characterization of plant chlorophyll relative content.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Huang

et al. 2021

Remote Sensing

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Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

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Section: Introductionmentioning

confidence: 99%

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Huang

et al. 2021

Remote Sensing

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“…Surprisingly, prediction for the chaff organ was comparable (R 2 = 0.94) to the top leaf organs, which might be related to the specific role of chaff in maintaining a high N accumulation by acting as a temporary sink and source for N (Kong et al, 2016). Also, senescent leaves and internodes achieved relatively low predictive power (Table 1), which was similar to vertical canopy N predictions in rice (He J. et al, 2020). Small variations in spectra and N concentration may account for the low prediction power in these senescence organs.…”

Section: Organ-level Predictive Modelsmentioning

confidence: 59%

“…In addition to NIRS, the spectrum at the visible (VIS) region associated with chlorophylls absorption can also reflect nitrogen variations (Asner and Martin, 2008;Meacham-Hensold et al, 2020). Recently, spectral reflectance acquired by hyperspectral sensor (VIS and NIRS) instruments has been increasingly used for predicting N concentration in leaves (Ely et al, 2019;Meacham-Hensold et al, 2020), shoots (Nguyen et al, 2019), grains (Caporaso et al, 2018a), and the entire plants (Li et al, 2010;He J. et al, 2020). By analyzing the full-spectrum data with chemometric modeling techniques such as partial least square regression (PLSR), nutrient elements (e.g., N and micronutrients) could be estimated from hyperspectral reflectance (Vigneau et al, 2011;Serbin et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes

Zhou

et al. 2021

Front. Plant Sci.

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Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration (Nmass) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate Nmass, the PLSR-based Nmass-spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions (R2 > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (ΔNmass) with an R2 of 0.90. ΔNmass of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.

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“…Due to the high data dimension of the hyperspectral information, it is usually necessary to perform a downscaling of the hyperspectral data before using the downscaled results to build a quantitative inversion model with the nitrogen content ( Chu et al, 2014 ). In order to accurately estimate the vertical distribution of nitrogen in the leaves of rice plants, He et al (2020) constructed a vertical distribution model of leaf nitrogen content using hyperspectral data by the vegetation index method combined with the height of rice plants to provide a technical basis for the vertical distribution of nitrogen in rice.…”

Section: Introductionmentioning

confidence: 99%

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential

Feng

et al. 2020

Front. Plant Sci.

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To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.

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Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data

Cited by 29 publications

References 46 publications

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential

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