Citation: Beeri, O.; Tarshish, R.; Pelta, R.; Shilo, T. Utilizing Optical Satellite Imagery to Monitor Temporal and Spatial Changes of Crop Water Stress: A Case Study in Alfalfa. Water 2022, 14, 1676. https://doi.org/10.3390/ w14111676 Academic Editor: Frédéric Frappart Received: 24 February 2022 Accepted: 21 May 2022 Published: 24 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). water Article Utilizing Optical Satellite Imagery to Monitor Temporal and Spatial Changes of Crop Water Stress: A Case Study in Alfalfa Ofer Beeri *, Rom Tarshish, Ran Pelta and Tal Shilo Science and Research Team, Manna-Irrigation, Gvat 3657900, Israel; rom.tarshish@manna-irrigation.com (R.T.); ran.pelta@manna-irrigation.com (R.P.); tal.shilo@manna-irrigation.com (T.S.) * Correspondence: ofer.beeri@manna-irrigation.com Abstract: Since the 1980s, thermal imagery has been used to assess crop water stress. The increase in the temporal resolution of optical satellite sensors (in the range of 400–2500 nm) and the better spatial resolution compared to the thermal imagery call for the definition of a new way for crop water stress monitoring. Hence, we are suggesting a new method utilizing spectral indices from three subsequent images to address this challenge. This method predicts the current water stress with the two past images and compares it to the current stress: if the existing conditions are better than the predicted stress, the crop is not under stress and has sufficient water for development. To evaluate the suggested method, we downloaded Sentinel-2 images and compared the stress found with that method to the leaf area index, leaf water potential, and yield from seven alfalfa growth cycles. The results outline the ability of the new optical stress index to depict spatial and temporal changes in the alfalfa water stress and especially illustrated the changes in the crop water stress over the growth cycle and after each irrigation. This new method needs to be validated with different crops and satellite sensors to verify its success. Keywords: Sentinel-2; leaf area index; leaf water potential; yield; optical stress index 1. Introduction Soil water stress (SWS) is an important factor in irrigation strategy. It represents the availability, or limitation, of the water in the soil for the crop. The Food and Agriculture Organization paper No. 56 [1] refers to the SWS as Ks (measured by soil sensors), which ranges from 1.0 for a well-watered crop to 0.0 when the lack of available soil water limits transpiration. Furthermore, in the irrigation strategy, Ks is used for irrigation scheduling and thus to increase the water use efficiency (WUE). In addition, the water-stressed strategy aims to achieve better yield quality [2–4], decrease plant diseases [5], or even reduce soil moisture to facilitate the harvest with heavy machinery [6]. Alfalfa (Medicago sativa L.) is a perennial crop with a non-stress strategy. Several studies have suggested that the biomass and yield increase with irrigation [6,7] or decrease when water stress is increasing [8]. Further, the timing of the irrigation not only affects the yield quantity and quality, but it is also important for weed control [9]. Yet, the need to decrease soil moisture before the harvest may develop stress at the end of the growth cycle or the beginning of the next one. This may reduce the following growth cycle’s biomass and yield. In parallel to the SWS, the crop water stress (CWS) is highly affected by the local climate. For example, the vapor pressure deficit (VPD) above 3.0 kPa increases the crop water stress dramatically [10,11]. Unfortunately, the spatial variability of climate parameters is greater than the field scale and, hence, does not allow revealing differences within the field. For in-field mapping of the CWS, remote sensing data are used via a combination of optical (400–2500 nm) and thermal (10,000–12,000 nm) imagery [12,13]. However, thermal Water 2022, 14, 1676. https://doi.org/10.3390/w14111676 https://www.mdpi.com/journal/water