# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods  
### Data Used  
The availability of data that is free and open for public use is a major challenge to developing countries in search of an effective solution at both national and regional levels at the early stages of disaster management. In this study, three primary annual time-series data sets were used to detect the nationwide 2015 flood in the transboundary river basin of Ganges, Brahmaputra, and Meghna (GBM) rivers. Emergency observation data with high spatial resolution images were also used when responding to requests from emergency users and agencies in cooperation with Sentinel Asia. Table 1 lists the used data including designed process for 2015 flood detection and the applicable data for near real-time flood mapping.  
First, the annual time-series data of Moderate Resolution Imaging Spectrometer (MODIS) were used to mainly detect floodwaters in the case of the 2015 flood. In particular, temporal and spatial changes of surface water bodies--focusing on floodwater--were detected for one-year flood mapping at an international river scale from the MOD09A1 (Terra) level-3 eight-day composite surface reflectance products, one of the MODIS products in the sinusoidal projection (swath: 2000 km, spatial resolution: 500 m), containing the best observations of bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648, 858, 470, 555, 1240, 1640, and 2130 nm, respectively), during an eight-day period [23]. Next, the level-3 MODIS Land Surface Temperature (LST; unit is Kelvin) product was also used to confirm surface water bodies and floodwaters collected within that eight-day period. Then, to improve the accuracy of flood mapping based on the Modified Land Surface Water Index (MLSWI), the 2015 water level data, collected at three stations (Sirajganj, Baghabari, and Bhagyakul of the Brahmaputra River) and provided by the Bangladesh Water Development Board (BWDB), were used. BWDB has a nationwide hydrological network, which measures surface water levels and water discharges five times a day [24]. This procedure compared a MODIS-derived water index (MLSWI) with in situ measurement data for verification at the representative training sites [25; 26].  
In addition, investigation was conducted on the availability of multiple satellite data in the Asia Monsoon region for near real-time flood mapping based on disaster framework shown by Figure 1. With typical sensors for water-related disaster risk reduction, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observing Satellite (ALOS-2 launched in 2014) Phased Array-type L-band Synthetic Aperture Radar 2 (PALSAR-2) data will be able to use for verification of the flood-affected areas and the maximum extent [27; 28]. In case of the 2015 flood, emergency observation imagery (i.e., ScanSAR mode: 350 km swath width and 100 m spatial resolution) was acquired at 05:53 AM (UTC) 5 September 2015 during the flood in response to requests from emergency users, including the International Centre for Water Hazard and Risk Management (ICHARM). Initiatives of international cooperation in space--i.e., International Chart and Sentinel Asia--have been providing observation data and nationwide monitoring in emergencies to mitigate disasters including transboundary river-basin floods [29; 30]. Also, Advanced Himawari Imagers (AHI: Himawari-8 was launched in 2014 and Himawari-9 in 2016) onboard the Japanese Geostationary Meteorological Satellite (GMS) as multifunctional transported satellites (MTSAT) have been provided as convincing data sources. Real-time Himawari 8 and 9 imageries will be able to contribute to the monitoring and forecasting of floods covering the East Asia and Western Pacific regions. The data of Himawari-8 imagery provides rapid assimilation of flood disaster big data having a file size of 329 GB per day, 930 MB per 10 min, whereas total data volume of MODIS data is just below 100 MB per day [31].