The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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 ($R^2=0.40$, $RMSE=0.26$).

Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψ_stem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψ_stem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψ_stem values with an R² of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.

The wine industry must face many challenges because of climate change. One of them is the increase in temperatures and droughts events. These changes sometimes lead to yield losses and can also impact the quality of the wine produced (e.g., increased alcohol content). The management of available water is also a sensitive issue as water requirements for vineyard irrigation are quickly increasing in the south of France. In this context, there is a need for a decision tool that can help evaluate the vine water status through the entire growth season at a large scale. To address this issue, we have previously developed a model (see Laroche-Pinel et al. 2021a) which predicts the vine Stem Water Potential (Ψstem) using Sentinel-2 (S2) images. This model was developed based on a field campaign over three years. The present study now aims to investigate the feedback of winegrowers on the outputs of our model. Therefore, it was applied on the plots of five wine estates that do not belong to the set used in the initial paper. The qualitative results show interesting spatial and temporal consistency in accordance with winegrower knowledge, irrigation data, and weather forecast. The predicted Ψstemhighlights spatial variability in vine fields where a water source emerges and reflects the differences between vine fields with a drip or sparkling irrigation or without an irrigation system. The predicted Ψstem also clearly reacts to a peak in temperature. According to their feedback, three of the five winegrowers would be glad to use this service in the years to come.

The current rise in temperatures is hastening grape ripening, resulting in unbalanced wines with high alcohol content but poor colour, aroma and phenolic composition. Late-season canopy manipulation practices aimed at reducing the size of the photosynthetic apparatus after veraison have been shown to delay ripening in cooler climates. However, these methods have not directly been compared in regions with high irradiance and temperature, where berry composition is negatively affected by late fruit exposure. In this two-year Cabernet-Sauvignon trial, we compared the application of pinolene (P) to late shoot topping (T) and late above-bunch zone leaf removal (LR), all performed during the late stages of ripening, with an untreated control (C). Vine water status was monitored weekly from the time of treatment application until harvest. Gas exchange was monitored only in 2021, from treatment application through to harvest. Berry samples were collected to measure berry weight, total soluble solids, pH, titratable acidity, and the anthocyanin profile. At harvest, we assessed yield components, rated sun damage on clusters, and produced experimental wines. Wine colour and chemical characteristics were evaluated. The application of P scarcely improved gas exchange and stem water potential. Conversely, LR and T increased water potential but did not affect leaf gas exchange. T was the only treatment that significantly decreased sugar accumulation, followed by P, which showed some effect although not significant. Grape yield was not altered, despite the use of P resulted in a lower berry weight in 2022. Canopy manipulation increased lighting conditions in the canopy and caused a significant presence of damaged fruits, especially in LR. Berry anthocyanins were not affected, but LR resulted in a lower concentration of total anthocyanins in the wine and altered the anthocyanin profile. P increased wine acidity, while LR and T improved wine colour. This study suggests that late-season canopy management can effectively control ripening speeds and improve the composition of grapes and wines. However, it is essential to avoid grape overexposure by employing the appropriate techniques to ensure grape quality is not compromised.