## 4 Discussion  
Management of spatial variability within a vineyard can increase profits significantly by allowing optimization of yield quantity and quality [52; 53]. Technologies and methodologies are currently being developed to allow variable-rate irrigation in drip irrigated vineyards and orchards [54; 55]. Since the current state of the art for irrigation decision-making uses time- and labor-consuming measurement of \(\Psi_{\text{stem}}\)[2; 35; 36; 56], remote sensing methodologies successfully acting as stem water potential proxies have particular appeal. That said, there remains a challenge in finding remotely acquired data that: (a) has sufficiently reliable correlation with \(\Psi_{\text{stem}}\), (b) is affordable and accessible, and (c) has both high spatial and temporal resolution, each of which are necessary in order for the method to be useful for high frequency irrigation scheduling. This study showed a significant correlation between vegetation indices derived from high-resolution Planet images (3-m) and \(\Psi_{\text{stem}}\) in Mediterranean (Israel) vineyards. Previous studies have shown that CWSI and NIR-based indices, like GNDVI and NDVI, may be good indicators of water status in grapevines through correlations with leaf stomatal conductance (\(g_{s}\)) and water potential (\(\Psi_{\text{Leaf}}\) and \(\Psi_{\text{stem}}\)). However, these relationships were mostly observed at the canopy- [22; 28; 29] or leaf [30; 31] levels using manual spectroradiometers or sensors mounted on small airplanes and drones. As far as the authors are aware, this is the first study that shows such a relationship using images of coarser spatial resolution acquired from satellites, and more specifically from Planet nano-satellites. One of the main challenges when using satellite information is the spatial resolution of the images, which is often coarser than the targeted object and may induce a mixed-pixel signal effect. For example, understory vegetation was shown to affect satellite spectral signals in complex vegetation systems comprised of multi-canopy layers [32]. The multi-canopy layer, vertical mixed-pixel effect was further corroborated in a simulation model by [PERSON] et al. [57], implying that understory vegetation may significantly affect satellite signals. In our case, understory expansion reaches its maximum at ~DOY 60, while vines budburst occurs later at DOY 80-90. Background effect in the vineyards, which may include the signal from soil and/or understory vegetation, was observed through a different VI-\(\Psi_{\text{stem}}\) linear fit in terms of magnitude of slope (slope ranged between 0.5 and 2), meaning that some  
Figure 10: (**a**) Observed and predicted \(\Psi_{\text{stem}}\) at Mevo Beitar vineyard for the 2017 growing season. Predicted \(\Psi_{\text{stem}}\) was derived using the site-specific per-week multivariable **MB-Mult** model, the site-specific **MB-Reg** model, and the **Global-Mult** model. Red and blue shaded bands in (**a**) indicate model uncertainties in **MB-Reg** and **Global-Mult**, respectively, while error bars in **MB-Mult** and observations denote \(\pm 1\sigma\). Scatterplot of observed vs. predicted \(\Psi_{\text{stem}}\) for (**b**) **MB-Reg** and (**c**) **Global-Mult**. vineyards experienced weak VI response to seasonal changes in \(\Psi_{\text{stem}}\), while others experienced a stronger response to \(\Psi_{\text{stem}}\) changes. This differential response is due to the fact that in vineyards with grapevines of more developed canopies, and consequently less exposed understory cover seen from the satellite, the VI signal is mostly coming from the vines while in vineyards with less developed vines much of the VI signal comes from the ground. To overcome this mixed-pixel effect, we used the difference between the VI signal at the POS, which corresponded mainly to the grapevines, and that of the understory derived at the Pre (\(\Delta\)VI).