## 4 Discussion
### General Discussion
The third major and most significant challenge is to progress from water balance components to crop productivity, and for this, accurate predictions of yields are an important step. There are two elements to this: the first is crop detection [63], and the second is the prediction of actual yields. Currently, the approach is to predict yields directly [64; 65] as, particularly in Australia and the US, more and more growers use precision agriculture and yield monitoring. However, predicting it from gross primary production (through evapotranspiration) would be more interesting, but this is limited by the variation in the conversion from biomass to yield between crops and locations. The fourth major challenge is around validation data. A recent review by [PERSON] et al. [12] highlighted that accurate determination of irrigated water use remains challenging. They particularly highlighted the lack of validation data that are available to verify the predictions. However, in the end, accurate predictions of water productivity are also in the interest of irrigators as water accounting can offer important clues for improving water use efficiency of irrigated systems. How Do We Achieve Accurate Water Footprint and Crop Water Productivity Estimates at All Global Locations? Arguably, there is already an existing global crop water productivity platform provided by the FAO: WaPOR V2 [14; 22]. This product provides estimates of crop water productivity based on remotely sensed ET, crop phenology, and biomass production/harvest indices at 250 m to 1 km scales. In terms of water use efficiency, this provides the Gross Production Water Use and Net Production Water Use indices [14]. However, as shown in the case study, while using ET can provide estimates of individual crop productivity, it cannot derive whole farm water footprints and whole farm irrigation efficiency (WFEI, [66]). For this, we still need understanding of the remaining water fluxes and storages at each enterprise. So far, this has only been achieved through detailed surveys (e.g., [66]). In the WaterSENSE project, the concepts related to WaPOR are combined with detailed quantification of other components, but this remains challenging. We have the remote sensing data and much of the ancillary data, but not always at the required spatial and temporal scales. Collection of validation data remains challenging, as there is a plethora of different spatial product offerings, especially in the evapotranspiration and vegetation remote sensing area. In addition, the commercialisation of irrigation management has resulted in a decline in government investment in irrigation research. Convincing local producers and irrigators to invest time and possibly money in collecting local data without direct outcomes can therefore be challenging, even if this will in the future result in improved irrigation management [12]. However, to be able to quantify water footprints and whole farm water use efficiency at any location in the world, there is a need to combine research efforts and connect the available spatial and temporal data sources at finer scales, and any available point scale data through an open data platform. Several current platforms already can play this role, but in the context of water, GEOGLOWS ([[https://www.geoglows.org/](https://www.geoglows.org/)]([https://www.geoglows.org/](https://www.geoglows.org/)) accessed on 20 February 2022) might be the best vehicle to capture such a global effort. A final major challenge is to quantify uncertainties with all the different water balance components. In this study, the uncertainties in the dam volume estimates related to the remote sensing pixel sizes were quantified [27], but for most of the other approaches the uncertainty remains unclear. This is, of course, not a simple challenge: accurately accounting for all uncertainties (including the observation uncertainties) is no simple task and at this point only approximate uncertainties can be estimated.