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.

For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study.