The Philippines’ Advanced Science and Technology Institute under the Department of Science and Technology (DOST-ASTI) has developed an AI-based and near real-time flood extent mapping service that utilizes C-Band Sentinel-1 SAR images. However, this method is limited by the availability of the Sentinel-1 images during flooding events. To address this issue, the institute, through its SARwAIS Project, utilized the S-Band NovaSAR-1 satellite, which was designed and launched by Surrey Satellite Technology, Ltd. With a 10% share to NovaSAR-1’s imaging capacity, the country can task image acquisitions that could help augment the Sentinel-1 datasets. Successfully captured images are prepared using the institute’s developed pre-processing workflow. Afterwards, a thresholding method, adopted from UN-SPIDER’s recommended practices for flood mapping, is employed to identify potentially flooded areas from these images. Generated products are then assessed to determine their relative accuracy in detecting potential floods. Satisfactory products are then distributed to relevant disaster management agencies and are also published in the agency’s social media page for further information dissemination. Python scripts were then developed to automate the established workflows, which were initially done manually. These scripts also help expedite the generation of flood maps especially when processing multiple SAR images. The acquisition and utilization of NovaSAR-1 images substantially help the country address the gaps on the availability of workable data for a more informative disaster response especially during flooding events.