Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region

  • R. Tosin Faculdade de Ciências da Universidade do Porto, Porto, Portugal and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
  • I. Pôças 1) Faculdade de Ciências da Universidade do Porto, Porto, Portugal; 2) Geo-Space Sciences Research Centre, (CICGE), Porto, Portugal; 3) Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal; 4) Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
  • I. Gonçalves Associação para o Desenvolvimento da Viticultura Duriense, Edifício Centro de Excelência da Vinha e do Vinho Parque de Ciência e Tecnologia de Vila Real, Régia Douro Park, Portugal
  • M. Cunha 1) Faculdade de Ciências da Universidade do Porto, Porto, Portugal; 2) Geo-Space Sciences Research Centre, (CICGE), Porto, Portugal; 4) Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
Keywords: handheld spectroradiometer; ordinal logistic regression; spectral vegetation indices; vineyard; grapevine water status.

Abstract

Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (ѱpd) measured by a Scholander chamber in two test sites of Douro wine region. The study was implemented in 2017, being a year with very hot and dry summer, conditions prone to severe water shortage. Three grapevine cultivars, 'Touriga Nacional', 'Touriga Franca' and 'Tinta Barroca' were sampled both in rainfed and irrigated vineyards, with a total of 325 plants assessed in four post-flowering dates. A large set of vegetation indices computed with the hyperspectral data and optimized for the ѱpd values, as well as structural variables, were used as predictors in the model. From a total of 631 possible predictors, four variables were selected based on a stepwise forward procedure and the Wald statistics: irrigation treatment, test site, Anthocyanin Reflectance Index Optimized (ARIopt_656,647) and Normalized Ratio Index (NRI711,700). An ordinal logistic regression model was calibrated using 70 % of the dataset randomly selected and the 30 of the remaining observations where used in model validation. The overall model accuracy obtained with the validation dataset was 73.2 %, with the class of ѱpd corresponding to the high-water deficit presenting a positive prediction value of 79.3 %. The accuracy and operability of this predictive model indicates good perspectives for its use in the monitoring of grapevine water status, and to support the irrigation tasks.

Published
2020-02-13
Section
Article