# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 1 Introduction  
The Ganges, Brahmaputra, Meghna (GBM) basins are one of the most flood-prone basins in the world. Due to being part of such big basins and most of the area being less than 7 m above mean sea level, Bangladesh faces the cumulative effects of floods due to water flashing from nearby hills, the accumulation of the inflow of water from upstream catchments, and locally heavy rainfall enhanced by drainage congestion [1; 2; 3; 4]. The country has a long history of destructive flooding that has had very adverse impacts on lives and property [5; 6; 7]. Approximately 20,000 deaths have been reported due to flooding from 1954-2007 [8]. An analysis by the Bangladesh Bureau of Statistics showed that from 2009-2014, 56.62% of households have been affected by disasters at least once [9]. Among them, 24.44% were affected by flood events. More than 80% of the country is flood prone [10]. In an average year, approximately 20-25% of the area of the country is inundated by floods, while in extreme years, the inundated area makes up more than 60% of the country [11]. During the 2017 flood, more than 30% of the country's areas were inundated, causing at least 134 deaths and affecting more than 5.7 million people [12]. Widespread frequent flooding is not only a profound problem for Bangladesh. Between 1995-2015, there was at least a $166 billion economic loss caused by different floods events around the world [13]. For effective response during flooding events, the rapid monitoring of flood situations, including mapping the extent of the inundation and damage, is highly critical [14; 15]. Before any flood event, flood forecasting and simulation of the inundation extent is critical for risk mitigation [16]. At present, flood early warning and monitoring information is produced by the Flood Forecasting and Warning Centre (FFWC) as water levels change in major river systems in Bangladesh [5]. The extent of inundation is also mapped by comparing the water level with the national digital elevation model (DEM). Generating inundation maps from hydrological models requires an up-to-date and accurate DEM as well as computing infrastructure to model the effects of obstacles on the flow of flood water in the floodplains. Unfortunately, a sufficiently high accurate DEM and infrastructure data are often not available [17]. Flood management based on water level forecasting is not effective in providing a spatially distributed flood area for the timely monitoring of flood events [13; 18]. Satellite-based monitoring of flood extent overcomes the limitations of the hydrological model-based approach [19]. Various attempts have been made in the past to map the flood extent in Bangladesh from satellite images [20]. [PERSON] and [PERSON] [7] attempted comprehensive flood extent mapping of Bangladesh in 1980 using National Oceanographic Atmospheric Administrative's (NOAA) advanced very high resolution radiometer (AVHRR) data. [PERSON], et al. [21] used MODIS surface reflectance images for flood mapping for the years 2004 and 2007. [PERSON], et al. [22] used Landsat-8 and MODIS to determine the impact of a 2017 flash flooding event on rice production in the Haor area. MODIS images also have considerable potential for daily agriculture flood mapping of Bangladesh [23]. Although optical images have great potential for mapping during good weather conditions with image analysis capacity and accuracy [16; 24; 25; 26], their use is limited to flood mapping in Bangladesh due to high (approximately 80%) cloud coverage during the monsoon and flood period. The average cloud coverage for Bangladesh was found to be 88.5% in June, 90.8% in July, 78.3% in August, 78.3% in September, and 17% in December (all at latitude: 24.921\({}^{\circ}\)N and longitude 91.869\({}^{\circ}\)E) [27]. In reality, the acquisition of cloud-free MODIS and Landsat optical images for flood mapping during flooding events is almost impossible. In April 2017, there were no cloud-free Landsat-8 images available for the study area for flood mapping.