# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
###### Abstract  
Sentinel-2 (S2) is widely considered a reliable satellite constellation for monitoring several crops, such as grapevine (_Vits vinifera_ L.). A large dataset of Italian vineyards randomly chosen was monitored with S2 from 2017 to 2022. Two vegetation indices (VIs) and their statistics were calculated from each vineyard. In addition, structural features and topographic information were assessed using Google Earth and national databases. The research study aims to identify the most relevant drivers of spatial variability by assessing the VIs among the whole dataset and the within-vineyard variability. The latitude and the vintage showed the most relevant effect on spatial variability, depicting the effect of daylight hours, climate conditions and weather events. However, the vintage did not affect the patterns of the within-field variability. Regarding grapevine management, training systems and the rows' orientation were relevant boosters of variability. While the vineyards planted with north-south-oriented rows reached the highest VIs values, the east-west-oriented ones showed the highest variability. Finally, an interaction effect was detected between hill or plain plantation and the terrain slope on both the average and variability of the VIs. The conclusions from the present study suggest the relevance of clustering vineyards under remote supervision according to the structural features to reduce data variability. Further studies should investigate other structural features or managerial properties. Sentinel-2; remote sensing; vineyard variability; vineyard management; vegetation indices; time series analysis +
Footnote †: journal: _AgriEngineering_  
0000-0002-0001-9999-22000-0000-00002-0000-0000-0000-0000-0000-0000-0000-00000-0000-0  
Many approaches were proposed to monitor and predict the vineyard's variability. In particular, remote and proximal sensing techniques are widely appreciated for saving time and manual labour compared to standard assessment through manual in-field scouting. In recent years, researchers compared several monitoring platforms, and then the accuracy of each platform was evaluated for the grapevine vigour mapping, demonstrating the efficiency of remote sensing (RS) [7]. In particular, satellites are listed as the cheapest and most affordable platform to monitor large areas with a sufficient spatial resolution [8]. Additionally, the effectiveness of using the satellite Sentinel-2 (S2) for retrieving accurate plant vigour maps has been proved [9]. Finally, a consistent correlation pattern among vegetation indices (VIs) measured through RS and the in-field grape quality was found in previous works [10,11]. VIs aim to give information about the plants' vigour and health status by comparing the quantity of light absorbed by chlorophyll and other pigments with the light reflected by vegetal tissues [12]. Chlorophylls absorb just a portion of the visible light spectrum while reflecting infrared radiation. Hence, the chlorophyll concentration in the vegetal tissue works as a tiebreaker in the ratio between the absorbed visible light and the reflected infrared [13]. The Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Red Edge index (NDRE) are assumed to be suitable VIs for describing plant vigour as well as the biomass production variability in vineyards [14]. The means of both VIs are based on the assumption that the amount of chlorophyll in plant tissue changes the ratio between the reflected infrared radiation and the absorbed red bands. The NDVI, in particular, is one of the most widespread tools for monitoring several crops. However, some limitations were stated related to saturation, especially when monitoring dense crops or in the case of bare soil. The grass cover may lead the NDVI to reach the maximum value (+1) in fruit crops; hence, the NDRE exploits the wavelength between the red and the infrared bands [15]. Compared to the NDVI, the NDRE usually ranges on a lower scale.