# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.1 Spatial Variability  
The orientation of rows and the training system were relevant across all vineyards' variability evaluation approaches. More specifically, rows' orientation can change the amount of absorbed light by the grapevine canopy [46]. The absorbed light is linearly correlated with the solar radiation in EW-oriented rows, whereas a divergent trend was verified for NS-oriented rows, in which the top leaves shade part of the canopy at midday. EW-oriented rows can also increase the grapevine's gas exchange, hence, the water consumption [47]. The above-mentioned statements could involve more practical outcomes in which NS-oriented rows may produce more biomass (plant vigour) and production (fruit yield) compared to SW-oriented rows [48]. Thus, those vineyards planted with NS and SW-oriented rows could have benefited from the direction of the intercepted light and the shadow made by the top leaves, which could explain the higher VIs reached in each season compared with the EW- and SE-oriented vineyards. For example, the NS-oriented vineyards reached an average NDVI 10% higher than the EW-oriented ones, which can practically be translated into a yield difference ranging from 5% to 10%. Because latitude and orientation showed a very weak positive correlation, any geographical pattern could be excluded, supporting the thesis of the row orientation effect on the grapevines' vigour and, consequently, the maximum VIs records. Moreover, the higher variability found in EW-oriented vineyards could be explained by the higher bearing of thermal and drought stresses than NS ones caused by the strong impact of the sun radiation. The same trends were replicated in each vintage, as shown in Figure 9. All these considerations are supported by the largest number of vineyards included in the dataset and their wide distribution in several cultivation areas. The training system effect is much more practically correlated with the amount of light reflected by the grapevine canopy. In particular, the HS improves the vines' light interception compared to the VS and AS [49]. Hence, the light reflected by HS vineyards was mostly retrieved by the grapevine leaves, whereas the fraction of grass and bare soil was much more relevant for VS and AS. Also, the variability in VS and AS was boosted by some common farm practices such as grass mowing and soil tillage (Figure 12). Additionally, the row's spacing is linked with the vineyard management and is weakly positively correlated with the average VIs while negatively correlated with CV. The row spacing was essential for plants to vent their biomass production for vigorous grapevine varieties. For this reason, vineyards planted at high row spacing could show higher VIs and are more stable than the others. Location and topography aim to describe the geographical position of the vineyards. The altitude and the soil layer inclination describe the two main factors affecting the plant's growth. Elevation and slope are also autocorrelated because the odds of finding a sloped vineyard increase with elevation (Figure A1). An increase in altitude causes a linear reduction in the average temperature, which can affect grapevine development [50]. In this context, the hilly landscape seems to limit the VIs' mean values and, at the same time, increase the data dispersion among vineyards (Figure 10). Similarly, a narrow negative correlation between VIs and both elevation and slope was stated [16]. The average terrain inclination can boost the soil spatial variability because of the soil composition [51] (Figure 11). Also, in steep slope areas, several soil layers can be found within the same field [52]. Additionally, for this research study, it was not possible to assess the side orientation of the sloped vineyards in the topography classification; hence, variable orientations could have enhanced VIs' variability [53].