Abstract. The goal of this study was to test the usefulness of high-spatial resolution information provided by airborne imagery and soil electrical properties to define plant water restriction zones within-vineyards. The main contribution of this is to propose a study on a large area representing the regions' vineyard diversity (different age, different varieties and different soils) located in southern France (Languedoc-Roussillon region, France). Nine non-irrigated plots were selected for this work in 2006 and 2007. In each plot, different zones were defined using the high-spatial resolution (1m 2 ) information provided by airborne imagery (Normalised Difference Vegetation Index, NDVI). Within each zone, measurements were conducted to assess: (i) vine water status (Pre-dawn Leaf Water Potential, PLWP), (ii) vine vegetative expression (vine trunk circumference and canopy area), (iii) soil electrical resistivity and, (iv) harvest quantity and quality. Large differences were observed for vegetative expression, yield and plant water status between the individual NDVI-defined zones. Significant differences were also observed for soil resistivity and vine trunk circumference, suggesting the temporal stability of the zoning and its relevance to defining vine water status zones. The NDVI zoning could not be related to the observed differences in quality, thus showing the limitations in using this approach to assess grape quality under non-irrigated conditions. The paper concludes with the approach that is currently being considered: using NDVI zones (corresponding to plant water restriction zones) in association with soil electrical resistivity and plant water status measurements to provide an assessment of the spatial variability of grape production at harvest.

<p style="text-align: justify;"><strong>Aims</strong>: The goal of this paper is to present the results of a study run over 7 consecutive years which aims at characterising the Temporal Stability of Within-Field Variability (TSWFV) for the most routinely measured vine parameters. In the context of precision viticulture TSWFV is of importance to know whether or not it is relevant to use the within-field variability of the year « n » to design a site-specific management strategy for the year « n+1 ».</p><p style="text-align: justify;"><strong>Methods and results</strong>: The experiment was based on 6 vine parameters measured at 30 sites located within a non-irrigated vineyard block. Parameters measured included indicators of a vine capacity to produce biomass (pruning weight, yield and size of the canopy) as well as indicators of harvest quality (sugar content, pH and Total Titrable Acidity). For each parameter a significant temporal variability of the field average was observed from one year to another. This temporal variability led us to define the TSWFV as the occurrence of consistently high value or low value zones within the field. The definition of high and low values is done according to the average field value of the year for each parameter.</p><p style="text-align: justify;"><strong>Conclusion</strong>: TSWFV analysis allows the parameters to be classified into two distinct types. Type 1 parameters (pruning weight, yield and canopy size) which present a significant TSWFV and type 2 parameters (sugar, Total Titrable Acidity and pH) which present no TSWFV.</p><p style="text-align: justify;"><strong>Significance and impact of study</strong>: For precision viticulture management these results are significant. They show that yield or vigour (pruning weight, size of the canopy) maps of the previous years are relevant in designing site-specific management strategies in the year « n+1 » or subsequent years. Conversely, maps of quality parameters from previous years are not useful in determining how to manage harvest quality in the year « n+1 ».</p>

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.

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