# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 4 Discussion
The derived maps provide crucial information for local disaster management agencies, which helps to prioritize relief and rescue operations. At the same time, automatically generated land cover maps derived from pre-flood Landsat-8 data support the assessment of economic loss and help in the prioritization of financial compensation due to crop damage. The use of SAR images provides significant advantages, as their cloud-free and all-weather capabilities enable the production of regularly sampled flood extent information in a near real-time delivery manner [7; 87]. Before the launch of Sentinel-1B, the revisit time of Sentinel-1A was 12 days, which was challenging for a rapid disaster response. After the launch of Sentinel-1B and the completion of the two satelliteSentinel-1 constellation, revisit times improved to six days over the area of interest. In the future, a constellation of three Sentinel satellites would be much more useful for responding to flooding events in near real-time. As part of this study, the team provided georeferenced GIS layers in addition to jpg-formatted maps to encourage the broad use of data and to ensure that the data are fully compliant with the GIS environments used by the response agencies [88]. For this study, the use of Sentinel-1 dual-polarization radar images showed a high potential for flood mapping due to their free-of-charge nature. Adopting the image pre-processing techniques and knowledge-based classification methods used in this study helped in developing scene-specific standards and knowledge and achieved better classification accuracy on flood maps [89; 90]. For this exercise, the developed flood mapping system was run on a desktop-type computer. Hence, the availability of sufficient computing hardware and internet bandwidth were the main limitations discovered in this study. For future implementations, we will consider the utilization of cloud computing resources, such as those offered by the Google Earth Engine, the ESA Thematic Exploitation Platforms, or Amazon Web Services-based environments offered by NASA's Alaska Satellite Facility (ASF) DAAC with all Sentinel images. At the same time, utilizing recently released public-domain Landsat-8 datasets via the Google Earth Engine or similar platforms may enable the establishment of a framework for rapid land cover monitoring on a national level [40]. The present study produced a flood inundation map with optimum accuracy for the entirety of Bangladesh. However, there were some uncertainties of flood maps due to the floating vegetated areas [91]. On the Sentinel-1 images, sometimes cultivated land for rice plantation was appeared as inundated areas. To overcome those uncertainties, local knowledge is crucial. Alternatively, unmanned aerial vehicles (UAVs) are emerging tools for the monitoring of real time floods in disaster management [92]. Due to flight endurance and payload capacity, UAVs would not be functional if large areas were inundated [69]. In general, SNAP tools take a longer time for preprocessing Sentinel-1 images. There is also a lag time between the image availability in ESA Sentinel hub and in GEE. If lag time is reduced in the future, the method could be implemented in GEE environment for a more rapid production of flood extent. Banglades is a flood-prone country and thus under constant threat of flooding. Every year, floods destroy lives, livestock, and infrastructure, bringing an enormous financial toll. During disasters, obtaining reliable information is crucial. As part of the operational methods, flood inundation was validated using Landsat-8 images. Unfortunately, due to the dominance of cloudy conditions, no (cloud-free) Landsat images were available during the peak flood period. Apart from wall-to-wall comparisons with optical or field-based studies, a determination of the extent of flooding in Bangladesh could serve disaster management purposes such as relief operations.