Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics

Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale

Aim: The aim of this short note is to provide first insights into the ability of Sentinel-2 images to monitor vine growth across a whole season. It focuses on verifying the practical temporal resolution that can be reached with Sentinel-2 images, the main stages of Mediterranean vineyard development as well as potential relevant agronomic information that can be seen on the temporal vegetation curves arising from Sentinel-2 images. Methods and results: The study was carried out in 2017 in a production vineyard located in southern France, 2 km from the Mediterranean seashore. Sentinel-2 images acquired during the whole vine growing cycle were considered, i.e. between the 3rd of March 2017 and the 10th of October 2017. The images were used to compute the classical normalized difference vegetation index (NDVI). Time series of NDVI values were analyzed on four blocks chosen for exhibiting different features, e.g. age, missing plants, weeding practices. The practical time lag between two usable images was closer to 16 days than to the 10 theoretical days (with only one satellite available at the date of the experiment), i.e. near 60% of the theoretical one. Results show that it might be possible to identify i) the main steps of vine development (e.g. budburst, growth, trimming, growth stop and senescence), ii) weed management and inter-row management practices, and iii) possible reasons for significant inter-block differences in vegetative expression (e.g. young vines that have recently been planted, low-productive blocks affected by many missing vines). Conclusions: Although this experiment was conducted at a time when Sentinel-2b was not fully operational, results showed that a sufficient number of usable images was available to monitor vine development. The availability of two Sentinel satellites (2a and 2b) in upcoming seasons should increase the number of usable images and the temporal resolution of the time series. This study also showed the limitations of the Sentinel-2 images’ resolution to provide within-block information in the case of small blocks or blocks with complex borders or both. Significance and impact of the study: This technical note demonstrated the potential of Sentinel-2 images to characterize vineyard blocks’ vigor and to monitor winegrowers’ practices at a territorial (regional) scale. The impact of management operations such as weeding and trimming, along with their incidence on canopy size, were observed on the NDVI time series. Some relevant parameters (slope, maximum values) may be derived from the NDVI time series, providing new insights into the monitoring of vineyards at a large scale. These results provided areas for further investigation, especially regarding the development of new indicators to characterize block-climate relationships

How to better estimate bunch number at vineyard level?

Despite the extensive use of sampling to estimate the average number of grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. This study characterised the effect of differences between sampling protocols in terms of estimation errors. The goal was to identify the most efficient practices that will improve the early estimation of an important yield component: average bunch number. First, the appropriateness of including non-productive vines (i.e., dead and missing vines) in the sampling protocol was tested; the objective was to determine whether it is relevant to estimate two yield components simultaneously. Second, sampling protocols with sampling sites of varying size were compared to determine how the spatial distribution of observations and potential spatial autocorrelation affect estimation error. Third, a new confidence interval for estimation error was determined to express expected error as a percentage. It aimed at designing a new tool for finding the best sample size in an operational context. Tests were performed on two vineyards in the South of France, in which the number of bunches per vine had been exhaustively determined on all the plants before flowering. The results show that the simultaneous estimation of number of bunches and proportion of dead and missing vines increased the estimation errors by a factor of 2. Despite the low spatial autocorrelation of bunch number, the results show that the observation must be spread across at least 2 or 3 sampling sites to reduce estimation errors. Finally, the confidence intervals expressed as a percentage were validated and used to define an adequate sample size based on a compromise between the expected precision and the variability observed in the first measurements

Temporal stability of within-field variability of total soluble solids of grapevine under semi-arid conditions: a first step towards a spatial model

Aims: This work focuses on the study of the intra- and inter-annual Temporal Stability of Within-Field Variability (TSWFV) of Total Soluble Solids (TSS) as an estimate of grape maturity. Methods and results: The experiment was carried out between 2009 and 2015 in four fields located in the Maule Valley, Chile, under irrigated conditions. Each field corresponded to a different cultivar (namely Cabernet-Sauvignon, Chardonnay, Sauvignon blanc and Carménère), and data collection ranged over two to four years depending on the field. A regular sampling grid was designed within each field, and TSS was measured at each site of the grid on different dates (from veraison to harvest). A Kendall test (W) was used to analyse the TSWFV of TSS between all dates for each cultivar and season. A Spearman’s rank correlation coefficient (rs) was used to analyse the relationships between each sampling date and the date of harvest considered as the reference. Results of the study highlighted high within-field variability in TSS. The W test showed significant intra- and inter-annual TSWFV, and rs values showed a high and significant correlation between sampling dates. Conclusion: These results are of interest for precision viticulture since, under the conditions of the experiment, the spatial patterns of the TSS maps obtained 40 days before harvest remain the same until harvest. Therefore, early target sampling of TSS may provide a good estimate of the spatial variability of grape maturity at harvest. Significance and impact of the study: The inter-annual stability of the TSS spatial patterns makes it possible to propose a simple empirical spatial model that allows estimation of TSS values for the whole field using only one reference measurement, provided that historical data are available

Viticulture de précision : variabilité spatiale et stabilité temporelle des paramètres quantitatifs et qualitatifs à un niveau intra-parcellaire

International audienceMeasurements of parameters spatialy positionned, with on line sensors mounted on classical machinery or airborne imagery is no more a problem in viticulture. In a short time, high resolution data dedicated to the assessment of the vine characteristics, the soil, the harvest, etc. will become a reality. This information sources will allow the wine grower to have a spatial accurate knowledge of the vineyard and its variability. Such an accuracy in monitoring the production system was never achieved until now. This work makes a brief overview of the tools and methods already released or under development to assess the vineyard variability of the main parameters. This work makes also an overview of the main references in vineyard variability obtained in the world. It presents the main results observed on yield, sugar, TTA, etc. variability. For each of these parameters clues on magnitude of variation and coefficient of variation observed at a within field scale are given. Assessing the within field variability can lead the wine grower to take advantage of this variability by adopting site specific management practices. In that case, information of the spatial structure of the variation is of importance since it gives an idea of how a site specific management is opportune on a field. This work will present the main results obtained in spatial structure assessment in viticulture (focusing on yield). Finally, the knowledge of the vineyard variability can lead the wine grower to adopt site specific management the year n on the basis of the variability observed the year before (n-1). In that case the temporal stability of the within field variability is of importance. This work presents main experimental results dedicated to the assessment of the within field temporal stability of the main parameters.La mesure de paramètres localisés géographiquement, en ligne grâce à des capteurs embarqués sur machine ou grâce à des images aériennes ne présente plus d'obstacles majeur en viticulture. A très court terme, la mesure spatialisée à haute résolution et systématique de paramètres sur la plante et le sol va donc devenir une réalité en viticulture. Ces sources d'information permettent d'accéder à une connaissance fine des systèmes de production qu'il était difficile d'appréhender avec des systèmes de mesure classiques. Ce document fait un rapide état de l'art sur les techniques existantes ou en cours de développement permettant d'appréhender la variabilité spatiale des principaux paramètres en viticulture. Il fait également un état de l'art sur les principaux ordres de grandeur rencontrés dans plusieurs vignobles du monde en matière de variabilité spatiale (amplitude de variation, coefficient de variation) pour les principaux paramètres. La connaissance de la variabilité intra-parcellaire peut amener à modifier l'itinéraire cultural. Dans ce cas une information importante est la structure spatiale de la variabilité afin de déterminer s'il est possible ou non de gérer les variations observées. Ce document présente les principaux résultats obtenus dans ce domaine en viticulture. Enfin, l'utilisation de la variabilité observée l'année n-1 peut s'avérer intéressante pour conduire la culture l'année n (stabilité temporelle de la variabilité intra-parcellaire). Ce document présentera les principales références obtenues en matière de stabilité temporelle à l'échelle intra-parcellaire

Viticulture de précision : état de l'art

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Le semi-variogramme : exemple d’utilisation pour l’échantillonnage spatialisé et l’estimation

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