# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 4 Discussion  
The advantages of rapid nationwide flood mapping are discussed in this section. The good agreement between the behaviors of SfWi and the water level suggests that the proposed synchronized floodwater detection algorithm can contribute to a comprehensive and instant understanding of a cross-boundary flood over Bangladesh and northeastern India. The algorithm makes it possible to identify large flood areas more accurately and rapidly than the single use of a water index, such as MLSWI, despite limited data availability in terms of data acquisition and weather conditions. Figure 5: Nationwide annual flood mapping using SfWi across international river scale, GBM Rivers: the maximum flood inundation extent (**a**) before the peak water level; (**b**) after the peak water level on 6 September 2015. However, a flood mapping algorithm--i.e., floodwater index and time-series image fusion--need further improvement with modified band calculation and combination of additional data based on better understanding of an actual flood situation and its characteristics. For example, water level data collected at over 80 widely dispersed gauge stations should be continually analyzed and converted to spatially- and temporally-distributed hydrological data with a river network. In addition, DEM-based flood simulation coupled with a distributed flood danger level can contribute to increasing the accuracy of estimating the maximum flood extent. In numerical flood simulations, it is also important to identify critical parameters--such as flow velocity, flood discharge, and sediment transport load--that affect the magnitude of flood propagation [36; 37]. Several satellite images, taken at different times from different types of sensors, are required to collect comprehensive risk information in order to detect a large volume of floodwater over widespread areas. Taking advantage of both optical (MODIS) and SAR (ALOS-2) data during flooding, due consideration should be given to two main challenges between two types of change detection: MODIS-derived index-induced change detection using different band combinations and SAR-backscattering intensity-derived change detection. In this study, the first challenge is to estimate accurate flood area using a pixel-based water fraction approach with multiple satellite data with different spatial resolutions, which influence the detection of water body in a pixel. The second challenge is to synthesize pixel-based images from multiple satellite data--i.e., optical and SAR imagery--and produce dynamic flood maps. Utilization of big data processing must be in place prior to a disaster in order to mitigate disaster risk. For example, the quasi-real-time Himawari 8/9 gridded data will be able to fully utilize an application of flood monitoring via big-data technologies. In line with the synchronized floodwater index (SfWi), floodwater pixels are identified and combined by multi-sensor image fusion after overcoming problems regarding data preprocessing associated with geocoded grid data, synthetic cloud-free data from a time-series cloud removal algorithm, and an identical spatial resolution from different bands. In the near future, flood risk mapping can be quickly provided as a low-cost solution with exposure and risk assessment for people and assets. Potential flood risk and damage can be computed for each grid in combination with indicators such as land use, population, and potentially relevant GIS data layers considering different flood situations and damage functions [38]. Therefore, flood risk could be provided to prioritize issues for public and private investment in disaster risk prevention and reduction based on an actual flood map through annual flood monitoring. In order to support risk information for public flood risk maps, flood maps are a fundamental element and help facilitate an evidence-based decision process in cooperation with central and local governments of disaster management.