## 1 Introduction  
Deficit irrigation is a commonly used irrigation strategy in vineyards aiming to achieve high-quality berries for premium wine production [1]. However, to optimize grape quality and production deficit, irrigation should be properly controlled. Proper irrigation management is also compulsory to achieve uniform quality of berries across the vineyard area. An effective method to guide irrigation management of wine grapes is continuous spatial (across the vineyard) and temporal (along the season) monitoring of grapevine water status [2]. Water status is typically quantified by measuring vine stem water potential (\(\Psi_{\text{stem}}\)), which provides an indication of the impact of soil water content on grapevine water status [3]. To achieve an accurate picture of the water status variability across the entire vineyard area, continuous \(\Psi_{\text{stem}}\) measurements are required from as many vines as possible. This task requires substantial human resources, making it time consuming. Emerging precision agriculture tools such as proximal and remote sensing offer possible replacement, or at least support, of conventional in-field monitoring techniques [4; 5]. Proximal and remote sensing tools may be used to provide the required information for irrigation management control [6], as well as to allow spatial consideration of irrigation needs across the crop field [7]. Remote and proximal sensing have been used for monitoring vegetation dynamics [8] and processes including water status [9; 10; 11], plant nutrient content [12; 13], plant diseases [14; 15] and crop phenotyping [16]. The information received from sensors mounted on drones or small airplanes can be applied to develop site-specific irrigation and other management decisions [17]. Thermal sensors mounted on airplanes or drones may provide information on plant water status through the use of the crop water stress index [18; 19; 20]. Crop water stress index (CWSI), which is based on crop canopy temperature [21], has been recognized as a reliable index for monitoring in-field water status variability within a vineyard area [22; 23; 24; 25]. CWSI has been shown to correlate with \(\Psi_{\text{stem}}\), though typically displaying different correlations for different dates along the season [26; 27]. Another approach is to use vegetation indices (VIs) derived from spectral bands as proxies for water status parameters and/or yield production. For example, [PERSON] et al. [28], found that the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI) derived from very-high-spatial resolution images (2.6 cm) were both correlated with pre-harvest leaf stomatal conductance (\(g_{s}\); \(r\) = 0.56 and 0.65, respectively) and yield production (\(r\) = 0.68 and 0.73, respectively) in a cabernet Sauvignon vineyard near Benton City, Washington. [PERSON] et al. [22] found strong correlations of spectral-based vegetation indices such as, GNDVI, NDVI and the modified simple ratio (MSR) with \(g_{s}\) (\(r\) = 0.84, 0.87 and 0.88, respectively) and \(\Psi_{\text{stem}}\) (\(r\) = 0.76, 0.82 and 0.81, respectively) using images acquired from an unmanned aerial vehicle in a commercial rain-fed tempranillo vineyard in Spain. Their results imply that VIs can serve as good water stress indicators in vineyards when using very-high-spatial resolution images (10 cm) that allow the extraction of pure canopy pixels. As expected, they also found high correlations of thermal data with \(g_{s}\) and \(\Psi_{\text{stem}}\); however, the correlation changed along the season [22], meaning that a per-date calibration should be derived in order to provide recommendations for irrigation scheduling throughout the season. [PERSON] et al.