Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation

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Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data (2011, background): “Blackburn (2007) found wavelet-based methods to be valuable for quantifying pigment concentrations across leaf and canopy scales from hyperspectral data.”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, methodology): “The inversion of PROSPECT-D was performed using wavelet transformations and a look-up-table approach (Blackburn 2007; Blackburn and Ferwerda2008; Cheng et al. 2011; Ali et al. 2015; Li et al. 2018).”