# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
###### Abstract
The NDVI and NDRE were also found to be the most precise indices for monitoring grapevine development and predicting key phenological stages [16]. The radiation reflected by terrain can be smoothed by using dedicated soil-adjusted indices. However, these indices did not prove a strength improvement compared with NDVI and NDRE in vineyards [14]. Additionally, researchers found a robust correlation between the NDVI measured through S2 from the whole vineyards compared with the NDVI measured directly from the segmented grapevine area thanks to a UAV survey [17]. In addition to monitoring yield and fruit quality traits, RS was a helpful tool for detecting the severity of extreme climate effects in vineyards [18,19]. For example, an algorithm was set up to predict the water status of plants using the S2 near-infrared bands (NIR) as a main data source and comparing them to ground-truth measures [20]. Dealing with vineyard variability is achievable if two axioms are verified in the field. The first axiom supposes vineyard variability is sufficiently spatially organised on the field surface. A structured variability between rows and plots makes dedicated variable-rate devices cheaper to use. The second axiom is that random and human effects are negligible compared to the other drivers of nonuniformity. In this latter case, the variability may be defined as spatially stable and manageable through agronomy [21]. On the other hand, the variability is defined as temporarily stable when vineyard variability is replicated across vintages [22]. Combining precision agriculture with good knowledge of plant physiology allows the identification of subplots of the vineyard for distinctive farming management and precise vineyard management through space and time to valorise the uses of the agricultural input [23]. In this context, trials were conducted to verify the feasibility of precision agriculture techniques to overcome issues related to the vineyard spatial variability with relevant income increases [24]. Hence, the focus was on improving nitrogen fertilisation. The effect of a variable nitrogen spreading was studied by changing the dose through three vineyard subzones representative of three grapevine vigour levels (total plant biomass) [25]. More practically, the researchers aimed to smooth the divergences of biomass production among three levelsof vines' vigour, which was estimated thanks to satellite images, hence obtaining a standard fruit production in the whole vineyard. Additionally, the variable application of pesticides was tested in the field, and the plant's canopy volume was estimated using RS techniques [26]. Finally, the S2 platform was also verified as a valuable tool to be included in a grapevine development simulation model [27]. Consequently, the benefit from precision agriculture would increase with the accuracy in variability assessment and management. Although the explanation of satellite images through agronomical and plant physiology notions was deeply studied, a consistent research gap was observed regarding the interaction between the vineyard's structural and topographic features with the RS monitoring. In particular, no leading contributors have verified the implications of the vineyard's structural features for satellite monitoring. Therefore, a certain level of uncertainty was detected while spatial data from agricultural areas were interpolated [28]. The relationships between topology, topography, and vineyards have been extensively studied since the past century. However, most research has focused on the impact of these factors on grape quality and yield fluctuations within specific vineyards or restricted production areas. Correlation and clustering analyses are commonly employed to demonstrate the effects of pedological or environmental factors on grapevine performance. Clustering analysis, in particular, is essential for identifying similar plots that share attributes such as plant vigour, grape quality, and yield.