Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Abstract

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

Grapevine recovery after fire and a first look at rapid damage assessment with satellite imagery

“…When comparing the coefficients of correlations between ground truth data and vegetation indices, A-C and H showed greater correlation with ground data, although still relatively weak. Normalised Difference Vegetation Index is the standard baseline vegetation index (Giovos et al, 2021), however, it only uses the red and near infrared bands. While, VARIgreen combines information from more bands (red, green and blue) and was included to investigate if coefficients of correlation improved with the inclusion of more bands.…”

“…To compare ground truth data and satellite imagery the correlation coefficient was calculated between ground truth fire damage data and post fire Normalised Vegetation Index (NDVI) and Visible Atmospherically Resistant Index Green (VARIgreen) (Gitelson et al, 2002;Cogato et al, 2021;Giovos et al, 2021). As some of the imagery used in this study consists of only red, green, blue and near infrared bands, VARIgreen's use of the red, green and blue bands is appealing as it makes use of the majority of the available spectrum coverage.…”

“…NDVI is a commonly used vegetation index which makes use of the red and near infrared bands. It has been used in studies relating to canopy growth and vigour (Xue and Su, 2017) and was found to be the most commonly used vegetation index in viticulture publications (Giovos et al, 2021). VARIgreen (often referred to as just VARI) was designed to be less sensitive to atmospheric conditions than NDVI (Gitelson et al, 2002).…”

“…VARIgreen (often referred to as just VARI) was designed to be less sensitive to atmospheric conditions than NDVI (Gitelson et al, 2002). Although, it was initially developed on corn and wheat crops, it has been used successfully in vineyard studies previously (Cogato et al, 2021;Giovos et al, 2021) and was investigated in this study.…”

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

“…Despite an increased use of UAS imagery in viticulture [57], few studies have been conducted to assess the grapevine nutrient status in vineyards and fewer with remote sensing imagery [58][59][60], specifically. Most studies involving the nutrient content in vineyards limit regression modeling to the associated chlorophyll content [59,60] or nitrogen [38,60].…”