Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression

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Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, background): “…studies, which demonstrate the superiority of indices that are targeted for specific variables or species over non-specific formulations (e.g., Cho et al., 2007; Fu et al., 2014; Hansen and Schjoerring, 2003; Huang et al., 2004; Li et al., 2014., 2008; Ramoelo et al., 2013; Yi et al., 2014).”
Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data (2017, methodology): “To date, many scholars, such as Yoder and Pettigrew (1995), Huang et al. (2004), Daniel and Gamon (2008), He, Liu, and Li (2014), and Li et al. (2014a, 2014b), have realized the hyperspectral detection of a large number of physiological and biochemical parameters of crops and vegetation, and…”