## 5 Discussion  
In this study, we sought to delineate the inundation extent across the coastal districts of Bangladesh, which extend over an area of approximately 47,201 square kilometers [48; 49]. This region includes a diverse array of land cover categories and ecosystems, such as built-up regions, croplands, estuaries, mangroves, beaches, and tidal flats. To detect inundation, we employed SAR imagery, focusing specifically on VV polarization. The choice of VV polarization was guided by its relatively higher probability of penetrating through vegetative canopy layers, which enhances the detection of inundated surfaces. In contrast, cross-polarization (VH) is more susceptible to depolarization due to multiple scattering events from heterogeneous land cover components within the radar's footprint. This scattering reduces signal penetration and complicates the interpretation of inundation extents. In comparison, VV polarization primarily interacts with vertical structures, which limits multiple bounces and reduces signal distortion, thereby improving the reliability of inundation detection. To evaluate the performance of VV versus VH polarizations, we conducted a comparative analysis for a typical coastal pourashava during both a cyclone event and a monsoonal day, as illustrated in Figure 11. The results indicate that VV polarization yields superior inundation mapping performance under both scenarios, confirming its suitability for operational monitoring of flood events in complex coastal land cover. Variations in SAR image quality play a critical role in determining the accuracy of flood extent mapping. Key factors such as spatial and temporal resolution, incidence angle, polarization mode, and frequency band influence the detection and delineation of inundated  
Figure 11: Comparison between inundation extent derived from VH vs. VV polarization of the SAR image for Chalna Pourashava. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Category of Inundation**} & \multicolumn{3}{c}{**Area (sq. km)**} \\ \cline{2-3}  & **Monsoon Rainfall** & **Cyclone** \\ \hline Low & 13,829 & 19,085 \\ Moderate & 4054 & 3459 \\ High & 1626 & 255 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of inundation due to monsoon rainfall and cyclones. areas, particularly in complex land cover settings like the coastal regions of Bangladesh. In this study, the SAR imagery used had a spatial resolution of 10 m \(\times\) 10 m, which may fail to capture small-scale inundations below this threshold. The temporal gap between pre- and post-event images ranged from 1 to 7 days (Table 1), potentially missing rapid flood dynamics driven by tidal influences. Given the study area's characteristically gentle slope (Figure 1), geometric distortions in backscatter values were considered negligible, although some errors may still occur in urban environments. Additional sources of error, such as radiometric inconsistencies and speckle noise, may also contribute to the misclassification of inundated areas. To address these limitations, ML techniques were employed to enhance classification accuracy. During the optimization of the change detection threshold, the \(f_{t}\), SVM model emerged as the most stable approach for inundation identification, demonstrating high accuracy, robustness, and strong discriminative capability. The differing accuracy values between the main evaluation and cross-validation reflect their distinct purposes: the former assesses model performance on a fixed test set, while the latter provides a more generalized estimate by averaging results across multiple data splits. Among the models, SVM consistently outperforms others in both evaluations, highlighting its robustness. This can be attributed to SVM's ability to maximize class separation margins and effectively handle non-linear data through the kernel trick. KNN performs well in the main evaluation but shows moderate consistency in cross-validation due to its sensitivity to feature scaling, noise, and data distribution.