## 1 Introduction
In addition, Sentinel-2 imagery has a spatial resolution of 10 m on the pixel side and a temporal resolution of 10 days; 5 days if we combine the images from the two existing satellites currently in the constellation. Sentinel-2 provides multiple bands from which to obtain information, including the Near-InfraRed (NIR) and the Red, which allow the calculation of the NDVI [42]. Remotely sensed images can be classified into two groups [43] according to their spatial resolution: (i) low-spatial-resolution imagery, in which the majority of pixels contain reflectance information from the grapevines and the inter-row space, and (ii) high-spatial-resolution imagery, in which the majority of pixels contain information only from grapevines or only from inter-row space. Therefore, when using low-resolution imagery, the NDVI value of the pixel is an indicator of the amount of vegetation (canopy area) rather than the NDVI from the canopy of the vines (pure value without the influence of the background). In this context, the main limitation of Sentinel-2 is that the spatial resolution and within-block information could not be accurate in the case of small blocks or blocks with complex borders [44]. This is a widespread problem in satellite imagery because within a vineyard pixel there are plants, soil and shadows, which influence the correct calculation of the coefficients of the crop [45]. More precisely, the NDVI obtained by the satellite and the LAI measured with a photographic ground-truth method can be related [46] and the images from the Worldview-2 satellite, with a resolution of 0.5 to 2 m\({}^{2}\), can be used to indicate that the amount of vegetation contained in a pixel varies according to its size. Therefore, with 0.5 m\({}^{2}\) it will be possible to find pixels with 100% vegetation, however, with 2m\({}^{2}\) there will only be mixed pixels of vegetation and soil. Instead of Worldview-2, Sentinel-2 can be used, but this will lead to a much greater problem, since, instead of a 2m\({}^{2}\) resolution, Sentinel-2 will have a resolution of 100m\({}^{2}\), so all the pixels will be mixed and will contain several plants as well as soil and shadows. It is important to note that soils have a great influence on the calculation of the NDVI [47] and that the average NDVI values for vines can be up to 3 times larger than the average NDVI values for pure soil [48]. Some authors [49] have established that pixels with an NDVI lower than 0.6 should be removed because they are not vegetation. Shadows are mainly influenced by the distance between plants and rows, but also by the characteristics of the plants [50]. Thus, in the same lighting, more vegetation will imply more shadow, an important factor since shaded pixels have low reflectance and modify the values we would expect if there were only vegetation and soil. Several authors have tried to solve this problem, trying to correct the shading in UAV and satellite images [51; 52; 53; 54; 55]. Additionally, NDVI can be greatly influenced by viticulture practices (e.g., canopy management and irrigation), so these practices should be considered [56]. Considering the inherent mixed-pixel characteristic of Sentinel-2 imagery in vineyards and the effect of missing vines (a vineyard, once established, generally loses grapevines each year due to diseases, abiotic stress, etc. ), the objective of the present study was to analyse the effect of missing vines on mixed pixels using the NDVI as a reference index (NDVI = (NIR - Red)/(NIR + Red)). Our approach was to work with real measurements of vegetation reductions at pixel level evaluated by Sentinel-2 satellites, in order to understand the relationship between the vegetation contained in a pixel and the information captured by the satellite.