The role of effective irrigation management for optimal food production is well recognized. This problem can be possibly solved through the improvement of water use efficiency (WUE) for irrigation to achieve sustainability in irrigated agriculture. At the farm level, to control the adequacy of the water applied to actual crop requirements, net irrigation water requirements (NIWR) are needed. Indeed, NIWR is the water that must be supplied through irrigation to satisfy evapotranspiration, leaching and additional water supply, which is not provided by water stored in the soil, as well as precipitation entering the soil. Specifically, computation of NIWR is based on the estimation of crop water requirements (CWR) and soil water balance, where crop evapotranspiration (ET_c) is the main component. There is a continuous research effort to estimate ET_c, CWR and NIWR. Irrigation water management generally requires continuous monitoring and reliable information at specific spatial and temporal scales about the soil and plant conditions across farms. Earth observation (EO) using remote sensing (RS) has already become an important tool for the quantification and the detection of the spatial and temporal distribution and variability of several environmental variables at different scales. Remotely sensed models are currently considered suitable for crop water use estimation at field, as well as regional scales. Having the knowledge of crop water requirements, irrigation can be supplied either to satisfy full requirements, or to manage a deficit-controlled irrigation.

At the present time, the most prevailing group of EO methodologies for the estimation of ET_c is the Energy Balance (EB) algorithms and more specifically the residual methods. Remotely sensed EB algorithms convert satellite sensed radiances into land surface characteristics, such as albedo, leaf area index, vegetation indices, surface roughness, surface emissivity and surface temperature to estimate ET as a “residual” of the land surface energy balance equation. Most recent EB models differ mainly in how Sensible Heat (H) is estimated. These models include the Two Source Model (TSM), where the energy balance of soil and vegetation are modeled separately and then combined to estimate total LE, the Surface Energy Balance Algorithm for Land (SEBAL), the Mapping Evapotranspiration with Internalized Calibration (METRIC) that uses hot and cold pixels within the satellite images to develop an empirical temperature difference equation and the Surface Energy Balance Index (SEBI) based on the contrast between wet and dry areas. Other variations of SEBI include the Simplified Surface Energy Balance Index (S-SEBI), and the Surface Energy Balance System (SEBS).

In this paper, a methodology is presented, which is developed for the estimation of the actual daily evapotranspiration (ETa). This is a contribution to a European-funded research project, namely "HubIS". The proposed methodology utilizes the combination of simulation programs and remote sensing. Indeed, obtaining useful spatial information and describing difficult physical processes through remote sensing is important for developing better agricultural practices. In this study, a combination of Sentinel-2 and Sentinel-3 images for daily crop evapotranspiration estimation is presented and applied in cotton fields in Thessaly Greece, which is considered as a water-limited Mediterranean agricultural area. The simulation program used is the Sen-ET SNAP software. Specifically, Sen - ET SNAP graphical user interface uses satellite images from Sentinel 2 and Sentinel 3 and meteorological data from the Weather Research and Forecast (WRF) model. The purpose of the Sen-ET SNAP plugin is to enable estimation of daily actual evapotranspiration (and other land-surface energy fluxes) at field scale. The applied approach has been first operated by the European Space Agency (ESA), using the Sen-ET plugin and the proposed methodology is an improvement of ESA’s method. The proposed methodology framework consists of seventeen (17) separate steps having as the outcome the actual daily evapotranspiration flows estimation at a 20x20 m spatial resolution. The proposed methodology is applied in cotton in Thessaly Greece for the 2021 and 2022 growing seasons. The results are very satisfactory and indicate the suitability of Sen-ET SNAP software to estimate daily actual evapotranspiration, as well as its spatial variability throughout the crop. The methodology can be applied for effective irrigation management in data-scarce rural regions.