# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
###### Abstract
Predominant factors like climate, latitude, and altitude significantly influence vineyard variability and spectral response. However, the impact of additional vineyard features not directly linked to soil composition or plant health status on the satellite monitoring of large vineyard datasets during the grape-growing season remains unclear. A research gap was identified in the limited understanding of how different vineyard structural features and environmental conditions affect the behaviour of spectral indices across large datasets, especially when ground data (e.g., fruit yield, plant age, irrigation) are unavailable. The effect of possible covariates on plant vigour, grape quality, and yield clustering is especially unknown. For instance, row orientation, which defines planting patterns, has been extensively studied from a physiological perspective in terms of gas exchange, sunlight interception, and photosynthesis efficiency. However, how row orientation might influence the RS of vineyards and whether it significantly affects VIs maps has not yet been thoroughly examined. Small pedological differences and microclimatic effects may be overlooked when analysing a large dataset, and these features could either amplify or mask the vineyard variability assessed through RS. Consequently, RS may overestimate or underestimate spatial variability between and within vineyards. The study aims to refine vineyard monitoring techniques by incorporating multiple variables and highlighting the need for high-resolution RS data to improve the accuracy of precision agricultural practices. This study focuses on vineyard variability within a large dataset of vineyards located across Italy, spanning multiple vintages. The study's main objective is to identify key factors influencing vineyards' spatial and temporal variability using an RS technique to improve vineyard monitoring. Specifically, the research focuses on understanding how vineyard characteristics like latitude, row orientation, training systems, and topography affect spectral indices and their use in precision viticulture. All these properties were manually measured for each vineyard. The vineyards were then monitored using S2 during the grapevine growing season, and the NDVI and NDRE were extracted on each available date. The power of the NDVI is mainly explained by the strong correlation of the index with the amount of green (active photosynthetic) biomass produced by plants [29]. Several alternative indices are currently available to correct the bare soil's reflectance or atmospheric influences [16]. However, the choice of NDRE is asserted by the sensibility of that index on green biomass evolution during the crucial mid-season for crops [30]. Thus, the choice of two complementary VIs was supported by their affordability and to have an impartial comparison between vineyards. The contribution of structural and topographic features to the variability of VIs across the entire dataset and within individual fields aims to minimise the loss of critical information in RS of vineyards from different grapevine developing aspects. Such a large dataset includes genetic variability (varieties), climates and microclimate areas, soil composition, farming practices, and many more sources of variability, including their interactions. Hence, outcomes would provide a strong interference between vineyard location and planting system for RS, which would not be ignored, knowing that each feature might hide in itself much more information especially related to the farming practices. Specifically, the study addresses three key research questions. First, we evaluate the trend of VIs and statistical variation indices across the Italian peninsula. Second, we assess the evolution of VIs during the grapevine growing season through linear regression analysis. Third, we analyse the stability of within-field vineyard variability over six years of monitoring. A feature selection analysis, including principal component analysis (PCA) and multifactorial analysis of variance (ANOVA), was conducted.