Stable Seasonal Trends in Satellite-Derived Vegetation Indices over Vineyards: Preliminary Results from Trinity Canyon, Armenia

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.

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Publication Details

Journal
Applied Sciences
Published
2026-07-16
DOI
https://doi.org/10.3390/app16147146
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Stable Seasonal Trends in Satellite-Derived Vegetation Indices over Vineyards: Preliminary Results from Trinity Canyon, Armenia

Anahit Khlghatyan, Andrey Medvedev, Fabio Dell’Acqua, Shushanik Asmaryan et al.
Applied Sciences
Remote Sensing in Agriculture
article

Stable Seasonal Trends in Satellite-Derived Vegetation Indices over Vineyards: Preliminary Results from Trinity Canyon, Armenia

Anahit Khlghatyan, Andrey Medvedev, Fabio Dell’Acqua, Shushanik Asmaryan, Andrea Bergamaschi, Vahagn Muradyan
article en

Abstract

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.

Applied SciencesVol. 16(14)
University of Pavia (IT), Center for Ecological Noosphere Studies (AM)
Zero hunger
Openalex Percentile: Top 8%
Remote Sensing in Agriculture
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