The use of environmental satellites to monitor large-scale processes and retrieve associated parameters for direct use in the management of resources and natural disasters is widely recognized. Moreover, there have been notable studies on integrating such data sets with land surface models to support scientific progress. In hydrology, ground based data are spatially very limited, numbers of stations are in decline at global scale and major parts of the World still remain largely ungauged to this date – many of them in countries with limited economic and technological resources. Better and faster visual evidence of a flood as it unfolds is thus expected to facilitate better and faster responses on the ground – a factor of particularly high relevance in Luxembourg, where river systems are known to be highly reactive to precipitation input and floodplains are still subject to ongoing urbanization. One element that limits the more widespread use of remote sensing though is the often-inadequate revisit times of current satellites and the time delay between image acquisition and the distribution of hydrology-related information.Cost-demanding solutions for more timely acquisitions include very high-resolution satellite constellations and are an on-going effort of several space agencies. A much cheaper alternative is to use low spatial resolution data thereby allowing a more frequent revisit time. On the basis of the perspectives suggested by recent studies and considering the need to provide near-real time data routinely on a global scale as well as setting up online data archives, it clearly follows that there is to date a lack of spatially and temporally distributed hydraulic parameters to support ongoing efforts in flood monitoring and modelling.Hence, the specific research questions we want to address in this project are:- How to implement a fully automatic processing chain for sequentially and efficiently extracting three hydrology-related parameters?- How to assess and communicate the uncertainty of these remote sensing-derived flood parameters?- How to effectively combine remote sensing techniques with hydrologic-hydraulic models?- How to evolve already existing regional, intermittent and opportunistic remote sensing-based flood monitoring into systematic flood detection, monitoring and prediction services operating at a global scale?We intend to contribute towards the development of a global and near real-time service of flood parameters. More precisely, we aim at developing a processing chain for extracting relevant flood parameters from recent SAR data acquisitions, thereby providing a benchmark data set of flood parameters from SAR imagery of various acquisition modes in the form of flooded areas, flood edges and water levels (with associated uncertainties). Our hydrodynamic modellers will access these flood parameters to investigate the potential that such data sets offer (i) for calibrating and evaluating flood inundation models, (ii) for periodically updating flood forecasting systems and (iii) for helping to mitigate flood risk in various forms.