Coupling effective variable selection with machine learning techniques for better estimating leaf photosynthetic capacity in a tree species (Fagus crenata Blume) from hyperspectral reflectance

Abstract

Numerous studies have attempted reflectance-based estimations of leaf photosynthetic capacity parameters using different statistical approaches. Although increasing attention has been paid to selecting effective variables for data-driven methods to assess vegetation parameters, there has been less attention to the estimation of leaf photosynthetic capacity. The primary objective of this study is to examine the potential of selecting effective variables for machine learning techniques to estimate leaf photosynthetic capacity in a typical temperate deciduous species (Fagus crenata Blume) from leaf hyperspectral reflectance or its spectral transformations, with or without additional leaf traits. The least absolute shrinkage and selection operator (LASSO) method was coupled to two machine learning models to extract the effective variables for assessing two key leaf photosynthetic parameters (Vcmax and Jmax). The results showed that two support vector machine (SVM) models, successfully extracted the effective bands by utilizing the LASSO method from the Der-Log-VIs-Traits and Der-Traits, exhibited the best performance for Vcmax (R2 = 0.66, RMSE = 9.73 μmol m−2 s−1, RPD = 1.72, and AIC = 524.86) and Jmax (R2 = 0.65, RMSE = 18.33 μmol m−2 s−1, RPD = 1.68, and AIC = 567.39), respectively, suggesting that the LASSO method can effectively locate important photochemistry variables from hyperspectral data. The models also performed better when based on the first-order derivative spectra, rather than from original or apparent absorption spectra. Furthermore, the results also revealed that the NIR and SWIR spectral regions are important for estimating leaf photosynthetic capacity. This study provides a useful reference for estimating leaf photosynthetic capacity from full-spectrum (visible through shortwave infrared) hyperspectral reflectance data.