## 5 Conclusions  
The relationship between stem water potential and vegetation indices was evaluated in Mediterranean vineyards through the use of high spatial resolution Planet nano-satellite imaging on the freely available GEE cloud computing system for the first time. Using JavaScript API in GEE allowed fast computation of the derivation of time series of vegetation indices for the area of the 82 vineyards, showing potentials for real-time monitoring in small-size agricultural fields. The four vegetation indices tested in this study (GNDVI, NDVI, EVI and SAVI) showed significant correlation with stem water potential, with SAVI displaying a slightly better performance, likely due to its capacity for reducing soil effects. Correlations using of the four vegetation indices were high for measurements across the vineyard area (0.89 \(<\)\(r\)\(<\) 0.82 for **MB-Reg** and 0.64 \(<\)\(r\)\(<\) 0.67 for **MB-Mult**) and along the season (\(r\) = 0.92 for both **MB-Reg** and **Global-Mult**), allowing predictions of stem water potential variability in space and time. A method to reduce the background effect of understory vegetation and soil, which accounts for the fractional cover of the vines, was also presented. The fractional cover of the vines was retrieved as the difference in the value of the vegetation index at the peak of season and prior to the appearance of green vegetation in the vineyard (\(\Delta\)VI). This \(\Delta\)VI, was negatively correlated with the slope of the vegetation index to stem water potential relationship, meaning that vineyards with a low fractional cover have weak VI response to changes in measured stem water potential. Using the \(\Delta\)VI allowed the development of a 'global' multivariable model for predicting stem water potential in vineyards with different background (soil and understory presence) characteristics. Overall, the approach presented here shows promise in exploiting these technologies, which include resolving the mixed-pixel problem in agricultural as well as in the ecological monitoring of natural vegetation systems from satellites. The tools developed here, i.e., the combination of Planet images and GEE platform, with the aforementioned mixed-pixel separation approach, may be also used for water status monitoring of agricultural fields other than vineyards. Finally, pending further verification and validation, the proposed 'global' model presented here may be used for real-time irrigation management along the season from the abovementioned weekly values extracted using daily Planet images. Using the 'global' model could substantially improve sampling efficiency of conventional water status monitoring. The methodology developed in this study should be further examined with supporting information on soil texture, LAI/fractional cover and understory vegetation. Moreover, a comparison with freely available satellite images like those from Sentinel-2, which have coarser spatial (10-20 m) and temporal (5 day) resolution than Planet, should be conducted. Conceptualization, D.H. and Y.C. ; Data curation, A.P. ; Formal analysis, D.H.; Funding acquisition, A.B.-G., V.A. and Y.C. ; Investigation, D.H., I.B., Y.N., A.B.-G., A.P. and Y.C. ; Methodology, D.H.; Project administration, V.A. and Y.C. ; Resources, I.B., A.B.-G., V.A. and Y.C. ; Software, D.H.; Supervision, Y.C. ; Validation, D.H., I.B. and Y.N. ; Visualization, D.H.; Writing--original draft, D.H.; Review & editing, D.H., Y.C., Y.N., I.B., A.B.-G., A.P. and V.A. This research was funded by the Chief Scientist Fund of the Ministry of Agriculture and Rural Development in Israel, grant number [20-12-0030]. The authors thank [PERSON] for fruitful discussions, [PERSON] and [PERSON] for assisting with GEE Java script and Planet's Clip API, and Planet Labs for providing API tools and images, as part of Planet's Education and Research Program.