## 4 Discussion
The performance of the DEM-based model is limited due to lacking a representation of the hydrologic processes, including the infiltration and routing processes that could account for the overestimated water surfaces in areas A and C. Even with the fact that the DEM-based method is considered for urban pluvial flood mapping, urban drainage needs to be included for mapping such a case with this DEM approach. Nevertheless, the maps generated from DEM data can still help developing countries where credible flood hazard maps (e.g., U.S. FEMA flood layer) are unavailable. To better leverage the application of the satellite-based water detection algorithms, it is suggested to perform localized sensitivity analysis to explore the validated threshold values (for both optical and SAR sensors), which will be used for rapid inundation identification in the high flood-risk area. It is also recommended to set up a standard workflow concerning water-related variables, data preprocessing, mapping algorithms, and validation metrics. Meanwhile, the DEM-based model can be improved by using high-resolution LiDAR DEM data as the input, in addition to considering the hydrologic and hydraulic processes of water routing, infiltration, and levee protection factors in the model. Land cover, land use, floodplain topography, and impervious surface percentage of the floodplain can be monitored and estimated using satellite observation and machine learning methods for up-to-date land surface monitoring to be further included in the DEM-based methods [65; 66; 67; 68]. Finally, we would like to discuss the usage of flood detection provided by the aforementioned methods. These automated methods are rapid and can be easily applied for locations with limited resources, compared with other conventional and extensive hydrologic and hydraulic analyses. However, the advantage and disadvantages are somewhat obvious as well. Only a rough extent estimation can be provided that might lead to inefficient disaster management and rescue efforts, especially during a natural hazard. A more effective way to mitigate the impact of flood disasters is to incorporate a flood forecasting capability, such as flood early warning systems [69]. Later, in February 2022, Madagascar was struck by another cyclone, and the communities affected by those floods were left extremely vulnerable to any other hazards. While the flooding threat is already substantial, several ongoing trends, including rising precipitation intensity, could result in significant increases in flood risks in the near future. For a high-concentration climate change scenario, estimates from 11 climate models converge on the conclusion that flood frequencies in the high flood-risk regions for Southeast Asia, East and Central Africa, and large parts of Latin America could increase substantially in this century [1]. Undoubtfully, remote sensing data during or after the events can also be integrated into the flood models as validation data for rapid response evaluation and data assimilation processes in both physical and data-driven methods [70]. Above all, flood hazard maps [71], flood early warning systems, and satellite-derived flooding information from this research will be valuable to augment the comprehensive information available for stakeholders and communities to perform efficient flood management.