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Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach

2017; Elsevier BV; Volume: 173; Linguagem: Inglês

10.1016/j.chemolab.2017.12.001

1873-3239

Celestino Ordóñez, Manuel Oviedo de la Fuente, Javier Roca‐Pardiñas, José Ramón Rodríguez Pérez,

Plant Water Relations and Carbon Dynamics

This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression. Our approach proved to have some advantages over these two testing approaches, mainly in terms of the computing time and the lack of assumption of an underlying model. Finally, the paper concludes that estimating water content from a few wavelengths is almost equivalent to doing so using larger wavelength intervals.