# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## Materials and Methods
### Methods
Thirdly, within the clamped image, mean radar backscatter (dB) values of the VH and VV bands were generated and used as input for an expert-guided classification of flood and non-flood. VH and VV-band backscatter (dB) statistics for flood and non-flood samples are presented in Figure S1. For this analysis, only two major classes, a \"waterbodies\" class and an \"others\" class, were considered. Box plots were used to show the statistical distribution of the data. A distinct separable backscatter value for water and other classes was used for image classification [44; 63]. To understand the quality of the produced thematic maps, a validation process was required for the classification results [39; 64]. During the flood disaster, it is challenging to conduct fieldwork for flood map validation [65]. Cloud-free Landsat-8 images (Landsat Surface Reflectance Level-2) available during the flood period (from 22 August 2017) was used for cross comparison. The Landsat-8 image acquired on 22 August 2017 was classified for flood mapping in eCognition developer using an object-based image analysis (OBIA) method called the geographic object-based image analysis (GEOBIA). The detailed methodology used to prepare the flood maps is described in [43]. Briefly, eCognition Developer software was used to divide the image into segments. The GEOBIA method segments remote sensing imagery into meaningful image objects based on the spatial, spectral, and temporal characteristics of image pixels. During the Landsat based flood mapping, few procedures were applied in terms of selected attributes using indices such as the normalized difference water index (NDWI), normalized difference vegetation index (NDVI), a and the land and water mask (LWM) derived from spectral values of the image, together with land band information. Finally, developed rule sets exploring the image object mean value were used to generate Landsat-based flood maps. The Landsat-based flood maps are shown in Figure 4c and were used to see omission and commission errors with Sentinel-1-based flood maps. A comparison was made between Landsat and Sentinel-1-derived inundation areas. Figure 4 shows the comparison of optical data and SAR-based flood inundation maps. In addition, 4500 reference points were collected from the Landsat-8 image of 22 August to validate the flood map of 29 August that was the closest available flood map from 22 August. The expert-guided classification scheme achieved an overall classification accuracy of 96.44%. As the initially detected flood extent includes both permanent water bodies and flooded water, pre-flood water extent must be removed from the classified map of flooded area [66]. Pre-flood waterbodies classified from Landsat images acquired before 27 March 2017 was considered as perennial waterbodies because no news reports about floods were received before that date [22]. Overlaying the pre-flood waterbodies with the April, June, and August flood maps, the flood inundation area was separated from perennial water bodies. Further analysis of the derived flood information was carried  
Figure 4: Comparison of optical data and synthetic aperture radar—(SAR) based flood inundation: (**a**) False color composite Landsat-8 image from 22 August 2017; (**b**) classification result based on Landsat-8 (dark blue: Perennial water; light blue: Flood inundation areas; green: Other areas); (**c**) false color composite Sentinel-1 image from 29 August 2017, showing water bodies in blue; (**d**) classification result based on Sentinel-1 data (same color assignment). out in the ArcGIS environment to perform damage assessment as well as associated statistical analysis at a 30-m resolution (flood area estimation, affected area estimation). As part of the flood damage assessment, pre-flood land cover maps for the year 2017 were prepared in GEE using Landsat images acquired between January and April of that year.