## 1 Introduction  
Remote sensing (RS) is a tool that allows information on distant objects to be obtained quickly and accurately [1]. A practical way to use remote sensing in viticulture is by using vegetation indices (VIs)and its potential relies on their ability to estimate grape quality and yield using spectral information [2]. The VIs are algebraic combinations designed to highlight the contrast of plant vigour and its properties (e.g., canopy biomass, absorbed radiation, chlorophyll content). These indices are based on the fact that healthy plants show a high Near-InfraRed (NIR) reflectance and very low red reflectance [3; 4]. The Normalized Difference Vegetation Index (NDVI) [5] has proved to be a useful indicator of the status of the vineyard with several applications, such as for sub-block management [6; 7; 8; 9; 10; 11; 12] and estimating the leaf area index, (LAI and can correlate with certain parameters such as total anthocyanins, total phenols, soil moisture, clay and sand content, berry pH, soluble solids, vine size and yield components [13; 14; 15]. NDVI has also been useful for establishing a correlation between Photosynthetically Active Biomass (PAB) and total phenolics and colour [16], assessing the water status spatial variability within the vineyard [17] and monitoring quality characteristics in table grapes [18]. Furthermore, within-field NDVI patterns are quite stable between seasons [19]. In RS, a key parameter to choose is the platform on which the sensor is mounted. At present, unmanned aerial vehicle (UAV) platforms have been extensively used for studying and exploring vineyards [20; 21; 22; 23; 24; 25]. In general, UAV offers the possibility to obtain high-resolution multispectral imagery, however, the benefit of the high resolution is restricted by some UAV limitations, such as stability on windy days/areas, as well as piloting capabilities and global navigation satellite system/inertial navigation system (GNNS/INA) quality [26; 27]. Also, regulations established in most countries might be a problem for properly developing the capabilities of UAVs [28]. Another issue is the cost of each operation, and above a certain scale size, an image taken by satellite may be more convenient than others [29]. In this context, satellites can be used for several applications, for example, mapping vineyard plant and soil water status [30], harvest prediction [31] and to analyse the spatial heterogeneity in the evapotranspiration [32]. Modern image satellite analysis allows the combining of information from different sensors mounted on different satellites in order to improve spatial resolution [33; 34], even in the presence of clouds [35], although it should be noted that not all sensors provide the same information. There are several satellites which are used to obtain spatial information and they can be divided into two main groups depending on the cost of the images: free-to-use satellites and paid satellites. Regarding free-to-use satellites, Landsat and Sentinel satellites can be very useful and they have been used for applications as disparate as detecting motions before a landslide [36], ice flow measurements and the quantification of seasonal ice velocities [37], to assess the bloom dynamics of almond orchards [38] and to classify vineyards according to their vigour [39; 40]. Some authors [41] have discussed the differences in the information collected from the Sentinel-2A MSI sensor and the Landsat-8 OLI sensor. They found that the MSI surface reflectance was greater than the OLI surface reflectance in almost all bands and that the MSI surface NDVI was greater than the OLI surface NDVI. In this sense, Sentinel-2 satellites (Copernicus Project of the European Space Agency) can be particularly useful due to their free status and the relative ease of access to their web platform ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))).