Abstract

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans using canopy hyperspectral reflectance in the visible to near-infrared (VNIR) range from 501 to 801 nm. The dataset included multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach). Model performance was assessed using Kennard–Stone validation and leave-one-year-out cross-validation. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS performed well for AGB under Kennard–Stone validation, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. Leave-one-year-out cross-validation showed that year-to-year transferability was more difficult for AGB than for LAI and CC. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and that selected VNIR bands can provide candidate spectral regions for simplified sensing of soybean canopy traits.

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