Using foliar spectral properties to assess the effects of drought on plant water potential

Abstract

Drought frequency is predicted to increase in future environments. Leaf water potential (ΨLW) is commonly used to evaluate plant water status, but traditional measurements can be logistically difficult and require destructive sampling. We used reflectance spectroscopy to characterize variation in ΨLW of Quercus oleoides Schltdl. & Cham. under differential water availability and tested the ability to predict pre-dawn ΨLW (PDΨLW) using spectral data collected hours after pressure chamber measurements on dark-acclimated leaves. ΨLW was measured with a Scholander pressure chamber. Leaf reflectance was collected at one or both of two time points: immediately (ΨLW) and ~5 h after pressure chamber measurements (PDΨLW). Predictive models were constructed using partial least-squares regression. Model performance was evaluated using coefficient of determination (R2), root-mean-square error (RMSE), bias, and the percent RMSE of the data range (%RMSE). ΨLW and PDΨLW were well predicted using spectroscopic models and successfully estimated a wide variation in ΨLW (light- or dark-acclimated leaves) as well as PDΨLW (dark-acclimated leaves only). Mean ΨLWR2, RMSE and bias values were 0.65, 0.51 MPa and 0.09, respectively, with a %RMSE between 8% and 20%, while mean PDΨLWR2, RMSE and bias values were 0.60, 0.44 MPa and 0.01, respectively, with a %RMSE between 9% and 20%. Estimates of PDΨLW produced similar statistical outcomes when analyzing treatment effects on PDΨLW as those found using reference pressure chamber measurements. These findings highlight a promising approach to evaluate plant responses to environmental change by providing rapid measurements that can be used to estimate plant water status as well as demonstrating that spectroscopic measurements can be used as a surrogate for standard, reference measurements in a statistical framework.

Keywords: Quercus oleoides; drought; leaf water potential; partial least-squares regression; reflectance spectroscopy; water content.