A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy

By Yuh-Jyuan Lee, Chwen-Ming Yang, Kuo-Wei Chang, Yuan Shen. 2008

91 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/5cc6f9a295b5188704414976f49bd6e2e57ef817

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Citations in the corpus

Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing (2011, background): “In the case of rice plants, the simple ratio of two narrow bands was studied to detect the biophysical, physiological or biochemical characteristics (Casanova et al., 1998; Kimura et al., 2004; Wang et al., 2007; Lee et al., 2008; Stroppiana et al., 2009), to estimate the gross ecosystem production ”
SWIR-based spectral indices for assessing nitrogen content in potato fields (2010, background): “Nitrogen (N) is an essential element for plant growth and productivity (Lee et al. 1999, Johnson 2001, Coops et al. 2003, Bonfil et al. 2004, Feng et al. 2008, Lee et al. 2008, Zhu et al. 2008) and is frequently the major limiting factor in agricultural soils (Daughtry et al. 2000).”
Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions (2021, background): “The SRI are numerical indicators that use either specific wavelengths or electromagnetic spectrum bands to quantitatively link changes in reflected spectrum with respect to physiological variables of plants [21].”
Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy (2009, methodology): “Lee et al. (2008) used the reflectance of 735 nm to assess nitrogen status of rice canopy.”
A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances (2013, methodology): “A simple index based on the first derivative of canopy reflectance spectrum at 735 nm was developed by Lee and collaborators [52].”
Estimating Rice Grain Yield Potential Using Normalized Difference Vegetation Index (2011, unknown): “Several studies have documented the potential use of remote sensing technology to monitor and evaluate rice N status (Xue et al., 2004; Lee et al., 2008).”
Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels (2013, background): “Importantly, the optimal spectral bands, as well as techniques to estimate Chl and N contents demonstrated in this maize study, are in accord with findings for rice (Inoue et al., 2012; Lee et al., 2008; Takahashi et al., 2000), wheat (Wu et al.”