Quantifying evapotranspiration (ET) is crucial for a valid understanding of the global water cycle and for the precise management of the resource. However, accurately estimating ET, especially at large scales, has always been a challenge. Over the past five decades, remote sensing has emerged as a cost-effective solution for estimating ET at regional and global scales. Numerous models have been developed, offering valuable insights into ET dynamics, allowing for large-scale, accurate, and continuous monitoring while presenting varying degrees of complexity. They mainly belong to two categories despite the variability of their empirical or physical components: temperature and conductance-based models. This comprehensive review synthesizes the fundamental theories and development history of the most used temperature-based models. It focuses on this specific category to maintain conciseness and prevent extended work. It describes the approaches used and presents the chronology of the modifications made and suggested by researchers. Moreover, it highlights the validation studies and the models’ advantages and drawbacks. The review addresses the long-standing challenge of accurately quantifying evapotranspiration at different scales, offers a retrospective comparative analysis spanning a 15-year period, and supports practitioners in selecting the most appropriate model for a specific set of conditions. Moreover, it discusses advancements in satellite missions, such as the Copernicus Space Component and Landsat Next, and their impact on enhancing ET estimation models.

According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (K_c) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.