Irrigation Intelligence is a practice of precise irrigation, with the goal of providing crops with the right amount of water, at the right time, for optimized yield. One of the ways to achieve that, on a global scale, is to utilize Landsat-8 and Sentinel-2 images, providing together frequent revisit cycles of less than a week, and an adequate resolution for detection of 1 ha plots. Yet, in order to benefit from these advantages, it is necessary to examine the information that can be extracted from both sensors to detect crop water potential. Our hypothesis is that these indices can be used successfully to depict significant changes in water quantity in commercial plots during the growth stage of the season, which may assist in monitoring crop water stress. Two data sets were used: published multi-spectral of full-stressed and non-stressed leaves, and satellite imagery with their corresponding leaf or stem water potential (LWP or SWP, respectively) of crop fields and orchards. Whenever possible, the leaf area index (LAI) as well as vegetation fractions were taken. Image processing includes the calibration to surface reflectance and calculation of known and new spectral vegetation indices (VIs). The ability of the tested VIs to capture water potential variability was developed in three steps: Firstly, the published dataset was used to present the sensitivity of each index to depict the differences between stress and non-stress at the leaf and canopy levels. These results not only show the magnitude of the relationships but also their direction (positive or negative). Secondly, we used our satellite imagery and field measurements datasets to report the statistical relationships among these spectral indices and the physical LWP or SWP over the growing season. The best index, which consistently depicts the differences, was employed in the third step, to map crop water potential in commercial plots. We tested these maps by measuring LWP or SWP in the extreme points (driest and wettest) and found significant differences among the points, although their canopy fraction or LAI were similar.