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Floods are among the most frequently occurring and disastrous natural hazards in the world. The overarching goal of this study is to investigate the utility of passive microwave AMSR-E signal and TRMM based precipitation estimates in improving flood prediction at the sparsely gauged Cubango River Basin, Africa. This is accomplished by coupling a widely used conceptual rainfall-runoff hydrological model with Ensemble Square Root Filter (EnSRF) to account for uncertainty in both forcing data and model initial conditions. Three experiments were designed to quantify the contributions of the AMSR-E signal to the flood prediction accuracy, in comparison to the benchmark assimilation of in-situ streamflow observations, for both “Open Loop” and “Assimilation” modules. In general, the EnSRF assimilation of both in-situ observations and AMSR-E signal-converted-streamflow effectively improved streamflow modeling performance in terms of three statistical measures. In order to further investigate AMSR-E signals' contribution to extreme events prediction skill, the upper 10th percentile daily streamflow was taken as the threshold. Results show significantly improved skill and detectability of floods as well as reduced false alarm rates. Given the global availability of satellite-based precipitation from current TRMM and future GPM, together with soil moisture information from the current AMSR-E and future SMAP mission at near real-time, this “first attempt” study at a sparsely gauged African basin shows that opportunities exist for an integrated application of a suite of satellite data in improving flood forecasting worldwide by careful fusion of remote sensing and in-situ observations.