Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics

Abstract

Monitoring in-vivo stomatal conductance (gₛ) dynamics is essential for predicting crop water usage and yield sensitivity in response to climate change. Leaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring; however, the underlying mechanisms connecting leaf spectra with stomatal anatomical and behavioral traits, and their subsequent impacts on gₛ, remain underexplored. In this study, we conducted a wheat field trial, collecting comprehensive measurements of stomatal anatomical (i.e., size, density) and behavioral (i.e., opening ratio, pore area) traits by a customized, high-resolution microscope, leaf spectra via a handheld spectroradiometer, and gₛvia a handheld AP4 Leaf Porometer across various genotypes, nitrogen treatments, growth stages, and diurnal environments. We observed substantial gₛ variability, with stomatal anatomical and behavioral traits jointly accounting for 79% of this variability. We further examined the relationship between leaf spectra and stomatal traits/conductance using a partial least square regression (PLSR) model and discovered that a single PLSR spectral model accurately predicted the variability of each of these traits and gₛ across our datasets. Furthermore, we demonstrated a strong correspondence between spectral variations resulting from gₛ and spectral alternation induced by stomatal anatomical and behavioral traits. By analyzing the diurnal association between spectral and gₛ variability, we revealed important biophysical mechanisms underlying relationships among spectra, stomatal anatomical and behavioral traits, and gₛ. Collectively, our findings highlight the potential of leaf spectroscopy in advancing crop physiology monitoring, contributing to enhanced food security and sustainability.

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