Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region

Abstract

Predawn leaf water potential (Ψpd) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy’s water status. Spectral data methods have been applied to monitor and assess crop’s biophysical variables. This work developed two models to estimate Ψpd using a hand-held spectroradiometer (400−1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Ψpd. Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Ψpd in a commercial vineyard in the Douro Wine Region. The first approach estimated Ψpd through vine’s canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVIopt1_950;596;521; SPVIopt2_896;880;901; PRI_CI2opt_539;560,573;716 and NPCIopt_983;972, as well as a time-dynamic variable based on Ψpd (Ψpd_0). The second modelling approach is based on pigments’ concentrations; several VIs were optimized for non-correlated pigments of vine’s leaves, assessed by its hyperspectral reflectance. The following variables for Ψpd estimation were selected through stepwise forward method: Ψpd_0; NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13–14%.