Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Isabel Pôças,Patrícia Costa,Luís Pereira,Igor Gonçalves,Mário Cunha,Arlete Rodrigues,Sara Gonçalves

doi:10.3390/rs71215835

Isabel Pôças, Patrícia Costa + Show 5 more

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https://doi.org/10.3390/rs71215835

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Journal: Remote sensing	Publication Date: Dec 5, 2015
Citations: 112	License type: CC BY 4.0

Affiliation: University of Lisbon, University of Porto

Abstract

Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r2) smaller than 0.67. However, the results of r2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R2 = 0.79 and a regression coefficient b = 0.96. Similar R2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

Highlights

In Mediterranean regions, where precipitation is scarce and irregularly distributed throughout the year, irrigation plays a major role in agriculture
An adequate management of deficit irrigation (DI) depends upon an accurate control of the crop water status, which is often done through measurements of predawn leaf water potential (Ψpd)
A straightforward statistical approach was used to verify the adequacy of vegetation indices (VI) computed with reflectance measurements in the visible domain aiming to detect and monitor crop water status in vineyard

Summary

Introduction

In Mediterranean regions, where precipitation is scarce and irregularly distributed throughout the year, irrigation plays a major role in agriculture. The use of Ψpd for irrigation scheduling is due to the fact that a quasi equilibrium between the water potential of plants and the soil occurs before the sunrise [3,4]. Some studies recently reported the occurrence of night-time transpiration in grapevine, resulting in incomplete predawn equilibrium between the water potential of plants and the soil, the response of such process has shown to be primarily affecting water use efficiency and is highly variable among cultivars and environmental conditions [5,6,7,8,9]. Like for other plant water status indicators, the measurement of Ψpd over large areas is labor-intensive and time-consuming due to the large number of observations necessary to accurately characterize a single plot. Non-destructive, accurate, and fast methodologies are desirable to assess crop water status and other parameters related with crop water stress or deficit [11]

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