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• Passive microwave signal can determine flood time span. • Observed surface water fraction presents a strong correlation with passive microwave signal. • Daily surface water fraction can be estimated using the proposed integrated method. • Spatial and temporal restrictions of remote sensing observations can be removed. Monitoring changes in flood extent is critical for flood control and mitigation purposes in areas where flooding affects many people and dense infrastructure and properties. Remote sensing can be an effective technique to detect changes in surface water extent and its dynamics. Compared to optical remote sensing, microwave information is suitable for working in any weather condition without severe cloud interference. Usually passive microwave data has a high temporal but a rather coarse spatial resolution, whereas for active microwave data this is reversed and only with ideal satellite constellation observations it can reach high sampling rates. To overcome these spatial and temporal restrictions, we proposed an integrated methodology to combine the passive and active microwave remote sensing and thus provide flood information more frequently at a high resolution. In this paper, a demonstration of the methodology is presented for a flood event occurring in Wuhan, Hubei Province of China in July 2016. The major inundation occurred along the Jushui River, part of the Middle Yangtze River basin. Using the brightness temperature data from a special data set (MEaSUREs), a daily passive microwave signal at the resolution of 3.125 km is used as an indicator to monitor flood occurrence and obtain the flood duration over time. Synthetic Aperture Radar (SAR) imagery (12-day revisit, 10-m spatial resolution) from Sentinel-1 was processed to estimate high resolution flood extents within the time span of the flood based on a threshold-based method together with the High Above Nearest Drainage (HAND) index post-processor. Surface water fraction data generated from SAR images presents a strong correlation with the passive microwave signal (R2 = 0.84) when using a quadratic polynomial fit. The average bias between surface water fraction computed by the passive microwave signals and SAR observations for water pixels within the Jushui River basin for the validation dates is 0.34%, indicating that this relationship can be applied to interpolate surface water fraction for each pixel along the river and other permanent water bodies for days when SAR observations are not available. This integrated method takes advantage of both passive and active microwave remote sensing and enriches temporally sparse flood data. Given the global coverage of the datasets used in this study, it can be utilized to estimate flood status for other flood-prone areas and thus contribute towards societal flood preparedness and response. [ABSTRACT FROM AUTHOR]