Exploring soil–plant interactions in vineyards using geophysics and hyperspectral imaging

Abstract

Climate change and evolving land management practices are reshaping soil–plant interactions critical for sustainable viticulture. These interactions are driven by soil texture, hydrogeochemical gradients, and climatic conditions, influencing grapevine traits like nutrient and water content. Integrating innovative methods, this study explores the relationship between soil variability and grapevine characteristics in the Médoc wine region, France. The research combines hyperspectral imaging, electromagnetic induction (EMI), and electrical resistivity tomography (ERT) with traditional soil and leaf sampling. Hyperspectral data, using visible-near infrared (VNIR) wavelengths, reliably estimated leaf traits such as nitrogen and water content, yielding strong predictive relationships (R2 up to 0.8). These findings suggest VNIR-based indices are cost-effective for monitoring grapevine physiology. Geophysical data revealed significant soil textural gradients, delineating sand, transitional (loam, sandy loam), and clay textural soil classes. Apparent electrical conductivity (ECa) and inverted electrical conductivity (EC) correlated with soil texture and grapevine traits, particularly at depths around 50 cm, aligning with primary root zones. However, interannual variability in correlations emphasised the influence of weather conditions and phenological stages, highlighting the need to align data acquisition with vine growth phases. The integration of hyperspectral imaging and geophysical methods provides a novel framework for linking soil and plant parameters. This interdisciplinary approach enhances the spatial resolution and scalability of vineyard monitoring, offering actionable insights for precision viticulture. Future work should expand datasets and refine predictive models to improve the understanding of soil–plant dynamics under changing environmental conditions. These findings underscore the potential of combining hyperspectral and geophysical data to develop climate-resilient vineyard management strategies, advancing precision agriculture, and sustainable viticulture practices.

Plant phenotyping relevance

ハイパースペクトル画像によりブドウ葉の窒素・水分などの植物形質を推定し、予測性能を評価している。土壌調査も含むが、植物形質の取得・推定手法が主要な技術的貢献である。

Code and data availability

The article describes leaf/soil sampling, EMI/ERT surveys, drone and ground hyperspectral imaging, and Random Forest modelling, but contains no public data deposit, no author analysis code release, and no trained-model checkpoint. References to 'Figure S1' and supplementary tables are figures/tables within the paper's

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