# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.4 Outcomes Relevance  
The final section of this discussion consolidates the key findings and highlights their practical implications while addressing certain limitations. The primary goal of this research was to identify essential factors for monitoring a large dataset of vineyards using RS. Although no ground data (e.g., fruit yield, specific varieties, irrigation, plant age) were available, the extended monitoring period contributed to the reliability of the study's conclusions. Moreover, the analysis utilised multiple images from the same season to optimise the statistical indices. Latitude emerged as the primary source of variability among vineyards, which was mainly due to differences in climate conditions and farming practices. The structural features of the vineyards, particularly row orientation, also played a significant role in the variability observed in VIs maps. Mixing vineyards with different row orientations can increase the dataset variability and reduce the accuracy of the analysis. For instance, NS-oriented vineyards typically exhibit higher NDVI or NDRE values than EW-oriented ones. VIs tend to increase more rapidly in NS-oriented vineyards. EW-oriented vineyards usually exhibit much more temporal and spatial variability than NS-oriented ones, revealing the response of physiological patterns to the spectral analysis conducted with S2. More spatio-temporal variability was also confirmed by the ANOVA performed on nugget and range. Moreover, while topographic features can be directly derived from a geographic digital repository, the row orientation is a specific grapevine's property that is difficult to predict. The grapevine training system also significantly impacts vineyard monitoring, although most Italian vineyards use vertical, single hedge-wall training. Additionally, the combination of location and topography greatly influences the behaviour of VIs during the growing season. Flat vineyards in plains often perform differently from sloped vineyards in hilly regions; therefore, they could be indistinguishable from RS in some cases. In this topic, a discrepancy between NDVI and NDRE was found, which underlined the key features of the two indices and noted the importance of using two complementary indices. Many key grapevine quality and productivity parameters are strongly linked to topography and spatial variability [56], which are factors supporting precision viticulture adoption. Vineyards with greater spatial variability benefit more from differentiated agricultural management practices and mechanical equipment. However, this study did not measure or classify certain common vineyard characteristics. Factors such as soil management, mechanisation versus manual labour, and terracing, which reduces the actual field slope, are crucial for a comprehensive vineyard description. While some of these factors can be assessed using RS with high spatial-resolution images [57], their inclusion would have added value to the outcomes. Nevertheless, accounting for these variables across a large dataset remains challenging.