# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 3 Results  
Developed national level pre and post-flood inundation maps for the entire country, during March, April, June, and August 2017, based on the Sentinel-1 images, are presented in Figure 5. The results show the presence of perennial waterbodies in March 2017 covering an area of 5.03% in Bangladesh. In April 2017, a total flood-inundated area was 2.01%, with most inundation was occurring in cropland (1.51%), followed by rural settlement and homestead orchard areas (0.21%), and other areas (0.29%). Similarly, more areas were inundated during the catastrophic June and August 2017 months, with inundation covering 4.53% and 7.01%, respectively. The 2017 flood of Bangladesh caused significant inundation of cropland, rural settlement, and homestead orchard, and other land use areas. Considering all crop-related land use and land cover  
Figure 5: Comprehensive flood inundation map of Bangladesh for the months of (**a**) March, (**b**) April, (**c**) June, and (**d**) August 2017.  
types (Figure 6), the percentage of inundated cropland was found to be 1.51%, 3.46%, and 5.30% in April, June, and August respectively. Significant inundation also occurred to residential property, public infrastructure, and fish farming ponds. A total of 0.21% of the rural settlement and homestead orchard areas were inundated in April, increasing to 0.47% in June, and 0.65% in August (Figure 7). The time series flood data in Figure 8 shows that for the April to August 2017 time frame, some of the areas experienced continuous inundation, while some areas were progressively inundated, and some recovered from the flood waters as time progressed. Within the April and June 2017 time frame, an inundated area of 257,729 ha was common for both months, while 410,853 ha were newly flooded, and 38,776 ha recovered from the flood inundation. From June to August 2017, 532,173 ha were common for both months, while 502,927 ha were newly inundated, and 136,406 ha recovered from the floods (Figure 8). Figure 6: 2017 land cover map of Bangladesh developed using Landsat 8 and Google Earth Engine cloud computing. A land cover map for the same year was also derived from Landsat-8 data for potential flood damage assessment. The derived map was produced from a cloud-free Landsat-8 composite between 1 January and 1 April 2017 and is shown in Figure 6. The map consists of nine classes, namely, tree cover (Madhupur forest, hill forest, and mangrove forest, as well as rural settlement and homestead orchard), grassland, cropland, barren area, built-up area, and waterbodies. Figure 8: Flood recession and rise areas of Bangladesh between (**a**) April and June, (**b**) June and August. Figure 7: Flood affected major land cover categories. Flood extent was derived from Sentinel-1 SAR data while land cover classes were extracted from cloud-free Landsat-8 imagery. Through the possibility of an automatic SAR-based processing chain, flood inundation mapping based on Sentinel-1 images have the potential to rapidly provide flood information for flood management. During flood mapping, a significant difference in backscatter values for waterbodies and non-water areas enables a separation between inundated areas and other land areas. Derived optimal backscatter ranges for flood inundated areas in Sentinel-1 images shows a clear distinction from other classes, as presented in Figure S1. The backscatter response in VH polarizations for inundated areas were between \(-\)24.25 dB and \(-\)17.4 dB for this area of interest. In VV polarizations, water bodies showed a range between \(-\)22.4 dB and \(-\)12.9 dB. The backscatter response in VH polarizations for non-water areas was determined between \(-\)16 dB and \(-\)9.6 dB, and in VV polarizations it ranged between \(-\)10.8 dB and \(-\)1 dB. Identified optimal ranges of backscatter values can be applied for the automation of flood mapping using Sentinel-1 images to produce flood inundation maps for areas of similar topography.