Operational activities in the field of flood monitoring and prevention benefit from the availability of synthetic aperture radar (SAR) images. The main advantages of SAR data are synoptic views over wide areas, day and night acquisitions independent of weather conditions, as well as a reliable and high frequency data acquisition schedule. The Copernicus program, European Union's Earth observation (EO) programme, opens the door to disruptive innovation in the domain of floodwater monitoring and, more broadly, emergency management, due to its Sentinel-1 SAR mission’s capability to systematically, globally, and frequently acquire high quality EO data at 20 m spatial resolution with a revisit time of 2-3 days over Europe. In order to rapidly translate the large volume of SAR data into floodwater maps and value adding services, the European Commission’s Joint Research Centre (JRC) recently added Global Flood Monitoring (GFM) products based on Sentinel-1 as a new component to its Copernicus Emergency Management Service (CEMS). The GFM products are obtained by processing all incoming Sentinel-1 SAR images within 8 hours after data acquisition to systematically monitor flood conditions at global scope. While past analyses were limited to pre-identified flood images in the framework of CEMS, the current implementation processes all incoming images in a fully automatic way, thereby eliminating the time required for necessary human interventions. To reach this degree of automation, the system takes advantage of the constantly updated 20 m Sentinel-1 data cube made available by the Earth Observation Data Centre (EODC) facilities. It is requisite that the Sentinel-1 based retrieval algorithm, as one of the core components of GFM, is both efficient and robust. Moreover, it is designed to balance two objectives: to detect water at high accuracy (i.e. permanent and seasonal water bodies, and floodwater), while minimizing the identification of false alarms due to water-look-alikes surfaces that can be confused with floodwater. To reach a high degree of robustness, an ensemble-based mapping algorithm is implemented, which combines three independent floodwater mapping algorithms driven by different approaches. 1) LIST’s algorithm that requires three main inputs: the most recent SAR scene to be processed, a previously recorded overlapping SAR scene acquired from the same orbit and the corresponding previously computed flood extent map. The change detection algorithm maps all increases and decreases of floodwater extent and makes use of this information to regularly update the flood extent maps. To do this, it uses a hierarchical split-based approach, region growing and an adaptive parametric thresholding. 2) DLR’s algorithm requires one scene as the main input and further exploits three ancillary raster datasets: i.e. a digital elevation model (DEM), areas not prone to flooding and a reference water map. To map flood extent, it makes use of non-parametric hierarchical tile-based thresholding, region growing and fuzzy logic. 3) TU Wien’s algorithm requires three input data sets: i.e. the SAR scene to be processed, a projected local incidence layer, and the corresponding parameters of a previously calibrated multitemporal harmonic model. Based on these inputs, the probability of a pixel belonging to the flood or non-flood class is defined. The final floodwater map is obtained by integrating the results of the three independently developed algorithms. Pixelwise flood classifications are based on majority voting, such that at least two algorithms are in agreement. To contextualize the ensemble-based observed flood extent maps, the GFM system also provides a reference water mask derived from multi-temporal Sentinel-1 data. The combination of the reference water mask with the observed flood extent product results in the observed water extent. The observed flood extent map is delivered with uncertainty values informing on the certitude of a pixel being classified as flooded. Moreover, an exclusion map identifies all areas where the detection of water using Sentinel-1 data is hampered by the presence of dense vegetation, urban areas, radar shadow regions, permanently low backscattering areas (e.g. sandy areas), and non-flood prone areas, i.e. those that have a Hight Above Nearest Drainage (HAND) value above 15 m. Finally, advisory flags are provided to make users aware of large-scale dryness and wet snow cover (both potential sources of over detection), or of wind (a major source of under detection). GFM is not only a system that systematically and fully automatically processes all images acquired by the Sentinel-1 mission in near real time, it also provides access to a global record of flood maps based on the processing of the entire Sentinel-1 collection since its start in 2015. This record provides valuable information to assess flood hazard and risk at 20 m resolution at a global scale. All these products are integrated in the Global Flood Awareness System (GloFAS), where end-users can visualize, analyze and download the data. The algorithm is currently being extensively tested for different regions all over the world. A first quantitative evaluation shows encouraging results in relation to the accuracy for delineating the evolution of water bodies and further improvements to increase the accuracy of the GFM product is ongoing.