Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 9; p. 1837
• Main Authors: Laroche-Pinel, Eve, Duthoit, Sylvie, Albughdadi, Mohanad, Costard, Anne D., Rousseau, Jacques, Chéret, Véronique, Clenet, Harold
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2021; MDPI
• Subjects: Agricultural practices; Algorithms; Artificial intelligence; Climate change; Data acquisition; Drought; Ecology, environment; Image acquisition; Learning algorithms; Life Sciences; Machine learning; Monitoring; Precipitation; precision viticulture; Rain; Reflectance; Regression models; Remote sensing; Ripening; Sentinel-2; Stems; Vegetation; Vines; vineyard; Vineyards; Water availability; Water monitoring; Water potential; Water shortages; water status; Water supply; Water use; Wineries & vineyards; Wines; France; precision viticulture; vineyard; water status; Sentinel-2
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13091837

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).