## 3 Results
### Vegetation Indices and Stem Water Potential in Vineyards across Rainfall Gradient  
The Pearson's \(r\) of the VI-\(\Psi_{\text{stem}}\) linear fit using all data from the 82 vineyards together was positive and significant (\(p<0.001\)), with \(0.58<r<0.68\) for the four VIs (Figure 6). Once again, SAVI was demonstrated to be the most successful index in tracking seasonal changes in \(\Psi_{\text{stem}}\) in the 82 vineyards (Figure 6d).  
The per-vineyard Pearson's \(r\) of the VI-\(\Psi_{\text{stem}}\) linear fits ranged between 0.72 and 0.97, with an average \(r\) of 0.85 \(\pm\) 0.06 (Figure 7). Linear fits, though, differed between vineyards in terms of intercept and particularly in slope (Figures 6 and 7).  
Figure 5: The difference between the \(\Psi_{\text{stem}}\) maps retrieved from **MB-Mult** and **MB-Reg**, both using SAVI (\(\Delta\Psi\)). \(\Delta\Psi\) is the absolute \(\Psi_{\text{stem}}\) value from **MB-Reg** minus the absolute \(\Psi_{\text{stem}}\) value from **MB-Mult**; therefore, blue color indicates a lower \(\Psi_{\text{stem}}\) value (more stressed) in **MB-Reg** compared to **MB-Mult**, while red color indicates a higher \(\Psi_{\text{stem}}\) value (less stressed) in **MB-Reg** compared to **MB-Mult**.  
Figure 6: Linear relationships between Planet-derived VI avg: (**a**) GNDVI, (**b**) NDVI, (**c**) EVI and (**d**) SAVI, and \(\Psi_{\text{stem}}\) in 82 vineyards (grey lines). The general linear fit using all data is also shown (black bold line).  
Figure 7: Pearson’s-\(r\), number of data-dates available along the season (N), slope and intercept of the linear fit of VI avg vs. \(\Psi_{\text{stem}}\) in single vineyards (see 82 vineyards in Table A1). The \(r\), N, slope and intercept were averaged over the four VIs. Error bars denote \(\pm 1\sigma\). Mevo Beitar vineyard is indicated in red.  
Background effects of soil and/or understory vegetation were likely responsible for the different slopes of the linear fits (Figures 6 and 7). Vineyards with a large background effect had more pronounced slopes, meaning weak VI response to seasonal changes in \(\Psi_{\text{stem}}\). This background effect may be noted as a negative correlation between \(\Delta\)VI and the slope of the VIs-\(\Psi_{\text{stem}}\) linear fit in Figure 8.  
Figure 8 shows that for vineyards with a small \(\Delta\)VI (weak VI response to \(\Psi_{\text{stem}}\) change), or, in other words, with grapevines of less developed canopies and thus a larger background effect, the slope of the VIs-\(\Psi_{\text{stem}}\) linear fit was steeper than for vineyards with a large \(\Delta\)VI. This suggests that \(\Delta\)VI is an important variable in predicting \(\Psi_{\text{stem}}\) and may assist in distinguishing between vineyards with different levels of background effect.  
The performance of each of the four VIs in a multivariable regression model (one model per VI) was quite similar (\(r=0.76\), for the four VIs), with a slight improvement (\(r=0.78\); RMSE = 18.5%; Figure 9) when using variables from different VIs in **Global-Mult** (Table 3). **Global-Mult**, though, was a significant improvement over the simple linear regression model used with the VI time series (\(0.58<r<0.68\), Figure 6).  
Figure 8: The relationship between the \(\Delta\)VI and the slope of the VI-\(\Psi_{\text{stem}}\) linear fit in the 82 vineyards. Notice that a larger slope signifies weaker response of VI to increasing stress (i.e., to decreasing \(\Psi_{\text{stem}}\)).  
As for the size effect (LogWorth) of each variable in the model, DOY was the one with the strongest effect (Table 3). The variable with the next-strongest effect was \(\Delta\)NDVI, meaning a strong background effect in the vineyards, as already suggested by the relationship shown in Figure 8. The SAVI avg had also a significant effect, comparable to the size effect of \(\Delta\)NDVI (Table 3).