# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 4 Discussion  
It is well known that Bangladesh has a long history of natural disasters. Between 1980 and 2008, it experienced 219 natural disasters that caused over USD $16 billion in total damage [77]. Due to the flat topography and climatic features, more than 80% of the population is potentially exposed to floods. Following the devastating disaster issues, Bangladesh has made significant efforts to reduce its disaster vulnerability primarily through post-disaster management. The Government of Bangladesh (GOB) recently constructed a good number of flood shelters, built flood protection embankments, sluice gates and regulators on different rivers, and have been dredging the drainage channels and canals [78]. Compared to previous efforts, Bangladesh is now much safer from disasters due to these post-disaster actions. Another survey of published literature concluded that considerably less research has been conducted for operational flood mapping using earth observation data for emergency response. Space based earth observation (EO) data can be used to deliver information on the extent of hazards during response operations so as to mitigate damage [79]. In the past, vital research was conducted for the 1988 flood mapping to support relief operations [7]. After that, few flood mapping studies have been conducted by academic researchers related to flood issues in Bangladesh without focusing on emergency response [6; 14; 21; 33; 35; 48]. Relatively, cloud-free satellite images showed that during the last three weeks of September, areas of inundation were 31% to 42% of Bangladesh. The actual flooded area was more from satellite image estimates which differed from officially reported areas [80]. Although real-time flood monitoring plays a vital role in relief operations [81; 82], flood maps also play an important role in decision-making, planning, and implementing flood management options [83]. Most of the studies have published their flood mapping results ten years after a flood event [32]. Sometimes the flood duration is quite long, although many researchers have mapped flooding areas for a single month. Furthermore, no dissemination systems were implemented to support sharing the inundated area maps during the crisis or after publishing the research article [84]. A number of monitoring systems have the potential for flood management in Bangladesh, however limited access to discharge data from the upstream, and lack of timely acquisition of geospatial data has an impact on the operational suitability of these systems. In many cases, model-based inundation mapping does not provide good results on plain land area [18]. Cloudy weather also prevents optical systems to provide coherent image coverage to use for flood inundation mapping. Due to the high level of cloud contamination during the monsoon time, cloud-free Landsat images identification was difficult for flood mapping [85]. For Bangladesh, one of the best opportunities for operational flood mapping comes from Sentinel-1 imaging as it is publically available [63]. In Bangladesh, the agriculture sector is greatly impacted by floods, despite conventional flood management systems paying very limited attention to this sector [63]. Under the circumstances that Bangladesh is under, the present study provides rapid flood inundation maps for March, April, June, and August 2017 using publicly available Sentinel-1 data using a replicable methodology. The derived flood maps provide spatial and temporal dynamics of the flooding area across the county. The utilization of the GEE for image processing facility in this study helped to promptly develop an operational land cover map for damage assessment. The advanced image processing tools of GEE enables the generation of accurate land coverage for large areas without downloading bulk data and prolonged desktop processing [86]. The method can be used for monitoring land cover regularly as the analysis can easily be re-run while new Landsat data is injected in GEE.