Typhoon flooding normally occurs suddenly with short duration, and the thick cloud cover limits the ability of one single satellite to timely capture the inundation extent. Landsat satellite data with a spatial resolution of 30 m is spatially applicable for flooding research; however, its 16-day observation frequency is typically insufficient to observe short-term typhoon inundation. Alternatively, despite the coarse spatial resolutions, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide daily data, which is well suited for flood-related investigations. Accordingly, the idea of combining these two sources of data to generate a high spatial and temporal image would be useful. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was applied to generate cloud-free Landsat/MODIS synthesized data with a spatial resolution of 30 m for the delineation of the inundated areas during a flood event. This approach produces a Landsat-scale image for fine-scale flood mapping of areas where there are no observed cloud-free Landsat or similar resolution satellite images. The fusion model was implemented on atmospherically corrected surface reflectance, and the resultant reflectance values were validated by comparing with observed Landsat reflectance before further data interpretation. The blending results indicate that the synthetic Landsat-scaled image is highly correlated with Landsat surface reflectance, captured a day after the synthetic image acquisition date, over cloud-free areas. For image interpretation, an object-based image analysis (OBIA) approach with an optimal-scale segmentation and the support vector machine (SVM) classifier was applied for flood classification. The flood mapping result was validated by comparing with a reference flood map derived from an observed Landsat image. This study demonstrates that the techniques of image fusion and object-based image analysis are useful for observing flood inundation in the heterogeneous vegetated area.