In 2022, flooding severely violated Australia, resulting in the displacement of residents and damage to property and public facilities. With the rapid development of information technology, it is possible to use Synthetic Aperture Radar (SAR) satellite remote sensing technology and the Quadrotor Unmanned Aerial Vehicle (QUAV) to detect and assess flooding environments. SAR can penetrate the cloud to operate at all times and in all weather, which is ideal for flooding area mapping. However, most SAR-based products are constrained by the flood’s dynamically shifting boundary and spatial and temporal resolution. QUAV is portable and capable of precise positioning despite being ineffective in covering large areas, such as flood-affected areas. Thus, it can complement the SAR counterpart for flood mapping in boundary extraction. This paper aims to propose a framework that mainly fuses satellite SAR and QUAV technology by aggregating the multiple-scale data for double validation and detailing, with enhancement by deep learning-based prediction models and a closed-loop feedback mechanism, to form a novel space-air vertical remote sensing framework. Finally, the selected flood-affected areas in Moama, NSW, Australia, were conducted as a case study. The results show that the proposed method can effectively enhance flood area assessment and mapping.