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Temporal Instability of Partial Least Squares Regressions for Estimating Leaf Photosynthetic Traits from Hyperspectral Information by Guangman Song; Quan Wang; Jia Jin is a Agricultural and Biological Sciences article available to read on EtoBox.

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Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R 2 = 0.45-0.84) and years (R 2 = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-

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Author

Guangman Song; Quan Wang; Jia Jin

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Agricultural and Biological Sciences (Life Sciences)