###### Abstract  
To increase water productivity and assess water footprints in irrigated systems, there is a need to develop cheap and readily available estimates of components of water balance at fine spatial scales. Recent developments in satellite remote sensing platforms and modelling capacities have opened opportunities to address this need, such as those being developed in the WaterSENSE project. This paper showed how evapotranspiration, soil moisture, and farm-dam water volumes can be quantified based on the Copernicus data from the Sentinel satellite constellation. This highlights distinct differences between energy balance and crop factor approaches and estimates that can be derived from the point scale to the landscape scale. Differences in the results are related to assumptions in deriving evapotranspiration from remote sensing data. Advances in different parts of the water cycle and opportunities for crop detection and yield forecasting mean that crop water productivity can be quantified at field to landscape scales, but uncertainties are highly dependent on input data availability and reference validation data. water use efficiency; Copernicus satellite data; irrigated agriculture 20221255022221 February 2022255022221 February 202225 February 202225 February 20222502  
Furthermore, there are major data limitations in capturing the desired detail in the water cycle, across large areas such as in the Western US [9] and the Murray Darling Basin in Australia [10]. Observations on-the-ground from gauging are sparse, and data on actual withdrawals and crop water use are often limited [9; 11; 12]. The problem is further exacerbated by economic rationalisation in many hydrometric departments, resulting in a decline in the actual number of observation points [13]. As a result, many global water footprint and water productivity studies are on very large spatial and temporal scales [3; 14] as the spatial data often are not available at finer scales. Typical scales for water footprints are 5 arc-seconds per grid cell for the spatial extent and monthly scales in the time domain [3; 15]. Alternatively, the data can be analysed at county level [16], which can still be large. Finally, water footprints or crop productivity analysed at a single point using crop modelling [17; 18] do not easily scale to the larger catchment or country scale. In contrast, improving on-farm water management and maximising green water use for crop productivity requires tools and quantification at much smaller scales from the field (paddock) to the farm scale [19]. In addition, irrigation scheduling applications require temporal scales that are shorter than a month [20]. All of this will require data at a much finer spatial and temporal scale than has been demonstrated so far [19], which is also needed if the focus is on policy compliance for environmental flow assessments [10]. However, investment in monitoring is expensive [21] and as a result the majority of irrigation systems remain unmonitored. Therefore, we need smart and new ways to observe the water balance at fine scales to enable improvements in water productivity (e.g., [22]). Luckily there are new approaches using finer-scale satellite data (e.g., Sentinel data as part of the EU Copernicus initiative) to observe the water cycle in much more detail [20; 23; 24]. However, as water information can only be inferred from satellite data rather than directly measured, there is a need for novel algorithms and direct testing against observed data. In addition, other data, such as data on soils, land cover, and digital elevation models (DEM) are generally needed to calculate and derive water cycle components [23; 24; 25; 26; 27; 28]. Some of these might again be derived from remote sensing observations [23]. Over time, many algorithms have been developed to estimate water balance components (for example, see [PERSON] et al. [20] for a list of ET algorithms based on remote sensing).