## 5 Discussion
These features may either mask floodwaters or exhibit backscatter characteristics similar to open water, potentially leading to misclassification. For instance, smooth surfaces or saturated soils--common in agricultural fields or the mangrove forests of the Sundarbans--may produce low backscatter values indistinguishable from inundated areas. Although visual inspection was employed to minimize such errors, the subjective nature of this approach may still leave room for misinterpretation in certain regions. Additionally, the use of threshold-based classification techniques (\(f_{t}\)) may not adequately account for local heterogeneity in land cover and hydrological behavior, particularly in complex coastal environments. The temporal resolution of available SAR imagery poses another challenge; the timing of image acquisition does not always align with the peak of rainfall or cyclone-induced flooding, leading to potential underestimation or overestimation of inundation extents. While efforts were made to select images closest to the event dates, image availability remained a constraint in several cases. For example, the inundation assessment during cyclonic events was based on imagery captured on the day of landfall; however, maximum inundation often occurs before or after landfall due to cumulative rainfall, tidal interactions, and storm surge progression. As such, the presented maps may not fully capture the temporal evolution of flooding. Furthermore, the application of spatial filtering techniques, such as focal mean smoothing, although effective in reducing speckle noise and enhancing image quality, can obscure smaller or linear water bodies, thereby underrepresenting inundation in narrow channels or localized low-lying zones. Another important limitation is the relatively small validation dataset, comprising only 71 locations across flood and non-flood classes. This limited ground truth data restricts the statistical robustness and generalizability of the classification accuracy. These limitations should be carefully considered when interpreting the flood frequency maps and their implications for disaster risk reduction and regional adaptation planning. We recommend that future studies adopt a multi-temporal analysis framework to more accurately capture the dynamic and rapidly evolving nature of cyclone-induced flooding. Enhancing spatial and temporal resolution through the use of high-frequency satellite constellations should be prioritized to better monitor short-duration and transient flood events. The integration of multi-source data--such as automated gauging stations, UAV-based observations, and other in-situ sensors--with satellite imagery through data fusion techniques can significantly improve the detection of rapid flood onset, particularly in tidal and densely populated urban regions. Additionally, the application of advanced ML and deep learning models--such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures--holds promise for improving classification accuracy and enabling more automated, scalable flood mapping across heterogeneous land cover. In the long term, SAR-based monitoring systems should be further integrated into dynamic flood forecasting models to enhance early warning capabilities and real-time decision support.