###### Abstract
However, many of the algorithms are limited in application to specific locations, specific crops, or specific landscape systems. For real applied water-cycle monitoring for water accounting purposes and crop productivity, the challenge remains to develop a library of algorithms that can be applied at any point on the globe at temporal and spatial scales that link to management. Despite the huge potential of remote sensing to observe the water cycle, challenges related to observing the water cycle have been well-documented over the last decade, e.g., [29; 30; 31; 32; 33]. Until recently, the spatial and temporal scale of satellite observations has been an issue [33; 34]. However, new satellite constellations, such as Sentinel (e.g., [23; 35]), have relatively finer spatial and temporal scales. However, an additional effect from the increase in temporal frequency and finer spatial scales is an increase in data volumes and processing. Some of this is alleviated by the development of cloud-based systems for spatial data processing, such as Google Earth Engine [36], and in the future it can be expected that more of these solutions will be developed, making processing, analysing, and displaying detailed satellite data easier. A further major issue, as highlighted by several of the recent reviews (e.g., [31; 33]), is the continued dominance of localised regression or other data-based techniques to derive relationships between satellite data and environmental data. This results in mostly local relationships in space and time, and often related to a specific land cover or water cycle element that cannot be easily extended to more global relationships. Far fewer papers focus on developing models that make use of the physical properties of the land cover or water cycle process and the way this is observed using the satellite sensors. For example, using the energy balance method (e.g., [37]), spatial contrasts in the observed landscape can be used to analyse energy differences and therefore estimate actual evapotranspiration. Such methods are more generalised and more widely applicable. Overall, remote sensing can be used quite effectively to observe fluxes and state variables from the water cycle [30]. Evapotranspiration, precipitation, soil moisture, surface water levels, streamflow, water quality, and groundwater storage can generally be observed through remote sensing [32]. These variables can be directly used in hydrological models, but are also of value for the agricultural sector, in particular for managing irrigation. Examples of products aimed towards irrigation in the agricultural sector are providing irrigated water use and water extent estimates [12], irrigation efficiency evaluations [38], water accounting [34] reservoir volume estimates [27], and footprint assessments [39]. From these papers, [PERSON] et al. [39], based on a quantitative literature review, also indicated that there are limited water footprint analyses using remote sensing. Many of the remote sensing-based water cycle observations can be (and have been) verified using in situ data. There has been extensive work on testing evapotranspiration (ET) satellite products against in situ data (i.e., [23; 40]), as well as several large scale campaigns to verify soil moisture products [41; 42], and large continental scale comparisons of the overall water balance [43; 44]. However, most of the verification studies rely on a limited set of publicly available global data or are based on relatively small local data sets which cannot be extrapolated easily to other areas. To generalise the prediction of water cycle using satellite data, there is a continued need to collect validation data, particularly if the prediction spatial scale is finer.