Monitoring leaf nitrogen status with hyperspectral reflectance in wheat

Feng, Wei; Yao, Xia; Zhu, Yan; Tian, Yongchao; Cao, Weixing

doi:10.1016/j.eja.2007.11.005

Cited by 162 publications

(90 citation statements)

References 47 publications

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“…The accuracy of the state variable simulations made using two assimilation variables was fairly consistent or even better than those using one variable (LAI or CNA). The primary reason for this is that LAI is a key variable for crop growth monitoring and yield prediction [58], and CNA is an important indicator of the N status of wheat and significantly affects photosynthetic production and grain yield and quality [59]. Various crop state variables are independent of each other, though they interact with each other [18,60].…”

Section: Discussionmentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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Abstract:The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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Section: Discussionmentioning

confidence: 99%

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Wang

et al. 2015

Remote Sensing

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“…The key model state variables, LAI and LNA, were selected as the coupling parameters for WheatGrow and remote sensing, which could not only express wheat growth conditions, such as canopy coverage characteristics and individual leaf nitrogen content, but were also directly related to the final grain yield. In addition, the data for both of these parameters could be successfully retrieved by remote sensing (Feng et al, 2008(Feng et al, , 2009Zhu et al, 2008). …”

Section: Wheat Growth Model (Wheatgrow)mentioning

confidence: 99%

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

Huang

Zhu

et al. 2013

Plant Production Science

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“…Passive remote sensing technology depends on the effect of the nutrient stress of crops on the characteristic wavelengths (Feng et al 2008). A number of papers reported utilizing of reflective spectra for monitoring the LNC of crops.…”

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confidence: 99%

“…However, utilization efficiency of N fertilization will decrease with increasing dose of N fertilization, which will cause environmental pollution and nitrate leaching issues (Olszewski et al 2014). Therefore, a large number of researches have been done to monitor the leaf N content by passive and active technologies for guiding the application of N fertilization (Feng et al 2008, Gong et al 2012.…”

mentioning

confidence: 99%

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

Yang¹,

Gong²,

Shi³

et al. 2016

PLANT SOIL ENVIRON

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Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R 2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

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Monitoring leaf nitrogen status with hyperspectral reflectance in wheat

Cited by 162 publications

References 47 publications

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Assimilating Remotely Sensed Information with the WheatGrow Model Based on the Ensemble Square Root Filter forImproving Regional Wheat Yield Forecasts

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

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