## 4 Discussion  
The results in Figure 3 and Table 3 indicate that Bmax Otsu and Edge Otsu generally produce similar results compared to those from Fuzzy Otsu. Even though the sampling processes to create the histogram are different, the threshold values from Bmax Otsu and Edge Otsu are nearly identical, especially in the cases of using Sentinel-1 SAR images, which is an indicator of the reliability of the threshold values. On the other hand, Fuzzy Otsu seems to be a more robust approach that allows the output to be nonbinary. While the Bmax Otsu and Edge Otsu show more water areas in the SAR imagery, the Fuzzy Otsu can detect more water areas in the optical sensors from Landsat-8/9. Between Bmax and Edge results, the flood inundation is slightly wider based on the Edge Otsu algorithm. More tuning efforts are needed to find the proper parameters and settings (e.g., spread value) to identify water pixels effectively. It is also noted that while the threshold values are estimated based on the entire image area, the result is evaluated only in part of the region (right corner). For some optical images that are heavily affected by cloud contaminations (Sentinel-2 and Landsat-7 images in this case), this will result in a limited area that is available for histogram sampling and will eventually change the threshold values. Therefore, it is critical to conclude that image quality is important for water mapping algorithms. In this study, Sentinel-1 delivers SAR images reliably every 12 days, which is invaluable for monitoring floods, especially considering the restrictions imposed by unfavorable weather conditions on the optical sensors; however, if an optical image of flooding can be acquired, obtaining useful information from this is straightforward and easier. In addition, the intercomparison of the SAR and optical sensors shows that the surface water areas identified from the SAR images (Sentinel-1) are more likely less than those retrieved from optical images. This may result from the images' features, such as the band or band-derived indexes (VV or mNDWI), image preprocessing and correction, and automated algorithms used in HYDRAFFloods. The newly available Landsat-9 data demonstrate the capability of effectively delineating flood-impacted areas, especially when the other optical images (Sentinel-2 and Landsat-7) were hindered by cloud contamination. Landsat-9 data will  
Figure 8: ALOS DEM, its differences with other DEM datasets, and the study area’s watershed and channel flow map. continue playing a critical role in adding timely information for flood monitoring, as the recently retired Landsat-7 mission has subsided. It is noted that the differences in wavelength ranges of Green and SWIR bands among Landsat-7, Landsat-8/9, and Sentinel-2 may also marginally change mNDWI values that impact the threshold for water surface mapping. Furthermore, high-resolution commercial images (e.g., Worldview, Pleiades, Planet optical imagery, and Capella and ICEYE SAR imagery) will provide more precise information when incorporated into the algorithms. Still, the increased pixel density brings in requirements and challenges of computational cost. This research also performs a rapid DEM-based water mapping practice using the FwDET algorithm in the GEE platform. The mapping results and quality vary with the resolution of DEM, as demonstrated by the differences from each other in Figure 7. Generally, the higher the spatial resolution of the DEM data is, the better the details can be simulated by the FwDET, as in ALOS and SRTM results. All the DEM mapping results can detect flood inundation at low elevations. However, with the land use information missing, the urban drainage characteristics cannot be represented in such DEM products.