# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 3 Results
### Within-Field Variability  
Finally, the third section of the Results debates the within-field variability, including the statistical analysis of each vineyard's semivariogram-specific indices (sill, nugget, range, and CI) as dependent variables with the structural and morphological features (factors). A semivariogram was computed for each vintage and vineyard, considering each pixel's seasonal average VI values inside the vineyards. For this reason, a CV is not included in this section. Moreover, to compute several semivariograms and to estimate the semivariogram's indices variability through a season may not be a conventional spatial analysis. The final section aims to answer the third research question, which is concerned with the structure and the temporal stability of the VIs variability measured inside the vineyards. Figure 17 summarises the outputs of the PCA performed on the kriging's indices compared to the vineyard's morphological features, excluding CI because it is an index derived from nugget and sill. The PCA also excluded the average NDVI and NDRE values because their contribution was partially covered by the \"latitude\" component. Therefore, the NDVI and NDRE revealed almost the same vectors' distribution, and the two VIs were split into two plots to avoid an excessive overlapping of information that could have deflected all the other vectors. First, the vectors' direction of the PCA performed on NDVI variability indices traced the same behaviour for the NDRE. In both plots, the PCA explained 27.54% of the whole variance. Nuggets were mainly related to latitude, space, elevation, and the shape of vineyards. A weak relation was found with orientation. Number of vertices and area represent the shape descriptors for the vineyards; apparently, these parameters were related to each other. Sill and range showed a predominant alignment with dimension and do not seem to be affected by any variables. Focusing on Table 7, the outputs of the ANOVA were equal between the NDVI and NDRE. Except for the direction, none of the variables demonstrated an apparent effect on sill, which means the total variability. The NS orientation showed the highest sill value. The exact output for the range was found. On the contrary, all variables were relevant for the nugget. Hence, the random variability (nugget) demonstrated a divergent trend compared to the total variability (sill). More in detail, NS and SW orientations recorded higher nugget values than EW and SE ones. Regarding the training system, the HS recorded the highest  
Figure 17: Biplots of the numerical morphological features along the first two principal components compared to the semivariogram’s specific parameters: nugget, sill, range, and CI. The semivariogram was fitted on the average VIs during the six-year monitoring period of each vintage. Chart (**a**) includes the indices referred to NDVI (NDVI Sill, NDVI Nugget, NDVI Range, and NDVI CI), while chart (**b**) includes the indices referred to NDRE (NDRE Sill, NDRE Nugget, NDRE Range, and NDRE CI). nugget. Finally, the flat vineyards in a hilly landscape showed a higher nugget than the sloped vineyards planted in a plain area. Figures 18 and 19 display some boxplots that refer to range and CI. Range and CI were both affected mainly by the direction. The range is linked to the size of the structured variability. The NS, SE, and SW orientations displayed in Figure 18a,b show a higher range distance than EW (NS range 126.80 m and EW range 96.39 m, respectively, for NDVI and NDRE). Focusing on CI, or rather the ratio between the nugget and the sill, showed a similar behaviour to the range. Figure 19a,b show that the NS and SW recorded the highest CI, which was followed by SE and EW each year and for both the VIs. NS reached the highest CI, while EW was the lowest (15.3% and 11.3%, respectively, for NDVI and NDRE). CI was also affected by the topography and training system. The boxplots in Figure 20a,b reflect CI distribution according to the topography classes.