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

Drought frequency is predicted to increase in future environments. Leaf water potential (Ψ_LW) 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 Ψ_LW of Quercus oleoides Schltdl. & Cham. under differential water availability and tested the ability to predict pre-dawn Ψ_LW (PDΨ_LW) using spectral data collected hours after pressure chamber measurements on dark-acclimated leaves. Ψ_LW was measured with a Scholander pressure chamber. Leaf reflectance was collected at one or both of two time points: immediately (Ψ_LW) and ~5 h after pressure chamber measurements (PDΨ_LW). 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). Ψ_LW and PDΨ_LW were well predicted using spectroscopic models and successfully estimated a wide variation in Ψ_LW (light- or dark-acclimated leaves) as well as PDΨ_LW (dark-acclimated leaves only). Mean Ψ_LW R², RMSE and bias values were 0.65, 0.51 MPa and 0.09, respectively, with a %RMSE between 8% and 20%, while mean PDΨ_LW R², RMSE and bias values were 0.60, 0.44 MPa and 0.01, respectively, with a %RMSE between 9% and 20%. Estimates of PDΨ_LW produced similar statistical outcomes when analyzing treatment effects on PDΨ_LW 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.