# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 1 Introduction
Similarly, during the period of 22 March-6 April 2017, MODIS images were not usable for mapping [22]. In Bangladesh, floods often occur when the sky is covered by clouds, thus making the utilisation of optical satellite images is infeasible in providing inundation mapping during the disaster. Therefore, spaceborne synthetic aperture radar (SAR) systems are the most preferred option for monitoring the flood condition. The introduction of SAR sensors has shown great potential for flood mapping due to their independence from solar illumination and very low dependency on atmospheric conditions [28]. Some studies have demonstrated that SAR images are useful for determining flood extent during a disaster [29; 30; 31]. An analysis of the benefits of using SAR images for flood mapping in Bangladesh was conducted using RADARSAT images from 1998-2004 and indicated strong applicability to flood response [32; 33]. Using six scenes of RADARSAT images from 1988 (July-September) and available GIS database, [PERSON], et al. [34] conducted a study on flood hazard mapping in Greater Dhaka. In support of ground data, [PERSON], [PERSON], [PERSON] and [PERSON] [32] analyzed RADARSAT images to produce inundation maps from 2000-2004 and proposed unique flood hazard maps of the north-eastern region of Bangladesh. Using the same RADARSAT images of 1998 and 2004, another flood mapping study was conducted for the Kurigram district of Bangladesh [35]. Considering the published literature, none of the studies produced flood maps (2005 onward) with national coverage for damage assessment using Sentinel-1 and ALOS PALSAR images. However, the availability of free SAR data through the European Space Agency's (ESA) Sentinel-1 C-band SAR mission created a major opportunity for flood extent monitoring in developing countries such as Bangladesh. For an estimation of flood damaged areas, pre-flood national level land cover is also essential. In the region, knowledge-based and object-based image analysis methods were used for the national land cover change assessment of Bangladesh [36], Bhutan [37], and Nepal [38; 39]. These studies were mostly conducted at decadal intervals for addressing national environmental issues but are not useful for analyzing flood damage assessment. Considering the amount of image downloading and processing required, the desktop based systems are not suitable for providing rapid processing services critical for a flood response. The cloud-based image processing platform from Google Earth Engine (GEE) is enabling the rapid processing of such big datasets covering a large area [40; 41]. The GEE has publicly made available large amounts of remote sensing satellite data collection and provides image analysis functionality at large spatial scales [42]. In 2017, unpredicted early heavy rain caused flooding in several parts of Bangladesh and damaged pre-harvested crops in April [22]. The flood started in April and continued until the last week of August, causing substantial damage to housing, property, and infrastructure. As part of the rapid response, the disaster management agencies were in urgent need of information about the inundated areas to prioritize their relief and rescue activities [43; 44]. This study aims to develop an operational methodology to support the response agencies by providing timely information on such inundated areas so as to prioritize emergency response activities. This study also aims to develop a methodology for potential damage assessment by analyzing pre-flood land cover maps automatically on the GEE platform. The inundation and flood damage data have been made publicly available for decision making processes related to flood management.