## 3 Results and Discussion  
### Satellite-Based Surface Water Mapping Results  
The surface water mapping results from each selected satellite imagery are shown in Figure 5a,b during the study period. The first rows of Figure 5a,b show the cloud-masked optical RGB images from Landsat-7/8/9 and original Sentinel-1 SAR greyscale images based on the VV band. The second rows show the mNDWI maps processed from the cloud-masked optical bands from Landsat-7/8/9, as well as the greyscale images from Sentinel-1 VV band preprocessed by speckle filtering and slope correction algorithms. The third to fifth rows show the final surface water maps derived from the second rows using the detection algorithms of Bmax Otsu, Edge Otsu, and Fuzzy Otsu, respectively.  
8 image (24 December 2021) indicates much more areas than the Sentinel-1, even though no rainfall occurred during the intermediate period. Sentinel-2 image (26 December 2021) shows similar coverage at the boundary of the study area to Landsat-8. However, it only presents limited areas due to cloud contamination. After several rainfall events occurred from 26 December 2021 to 1 January 2022, the Landsat-9 image (1 January 2022) presents additional water surface areas, especially in the northern region, where the Landsat-7 image at the same date also shows similar water surface areas if not covered by the clouds. Compared with Landsat-7 and -9 images on 1 January 2022, the Sentinel-1 image of 3 January 2022 still shows limited surface water area. Sentinel-1 (15 January 2022) shows more surface water in the western region due to the consecutive rainfalls between 3 January 2022 and 15 January 2022.  
For the second period (27 January 2022-2 February 2022) shown in Figure 4(b), more areas are identified as water surfaces, such as the map from Sentinel-1 on 27 January 2022. However, Sentinel-1 shows many fewer flooded areas in Regions A and B, marked by the reference map, indicating that the results from Sentinel-1 (27 January 2022) underestimate the flood area. Meanwhile, Sentinel-2 (30 January 2022) and Landsat-9 (2 February 2022) show a similar flooded area in term of its coverage in Regions A and B. Landsat-9 provides a more comprehensive perspective than Sentinel-2 with less cloud coverage.  
Additionally, the histogram of the sampling and the identified threshold values are presented in Figure 6. While some of the satellite images (e.g., Landsat-9 on 1 January 2022) show similar distributions in the histograms among different algorithms, most of the histograms are dissimilar, resulting in different threshold values. The results from Figures 5 and 6 are summarized in Table 3. It is noted that the midpoint threshold values by the Fuzzy Otsu algorithm are calculated as the arithmetic mean of the \"Max\" and \"Min\" values. Additional information on the threshold values for each Otsu algorithm is also included. Among the Otsu algorithms for the same dates, the SAR image (i.e., Sentinel-1) shows a pattern of Edge > Bmax > Fuzzy for both water surface area and threshold values, while for the optical image (e.g., Landsat-7, -8, and -9), a pattern Fuzzy > Bmax > Edge is shown regarding the threshold values, and Fuzzy > Edge > Bmax is shown concerning the water areas. For the Sentinel-2 images affected by the cloud contamination, the Fuzzy surface areas are found to be less than those from Edge and Bmax algorithms. For the same algorithm among different dates, the threshold also varies based on the inundation areas: (1) SAR Sentinel-1 images show the lowest threshold values in no flood (22 December 2021, 3 January 2022), lower values in less flooded (15 January 2022), and highest value in most flooded (27 January 2022) conditions; and (2) optical images from Landsat-7/8/9 and Sentinel-2 images also show higher threshold values in flooded condition than less flooded conditions.  
Figure 6: The histograms and the threshold values identified by each algorithm for the satellite imagery.