Improving the Flood Inundation Mapping Technique using Satellite Imagery and Remote Sensing Data: A Case Study of the Bihar Flood Caused by the Koshi Embankment Breach

Floods are considered one of the most damaging natural disasters known to humankind, and their severity has increased significantly due to the impacts of climate change and global warming in recent decades. Floods have become more unpredictable and erratic due to the influence of extreme hydroclimatic events. Therefore, obtaining near-real-time and accurate flood inundation maps of such events is essential for effective flood emergency response. These can be achieved easily by leveraging multi-source satellite imagery and remotely sensed data. The freely accessible satellite imagery and remotely sensed products can provide essential information that can significantly reduce the resources needed to create flood inundation maps and improve the accuracy of mapping and monitoring systems. This study integrated high-resolution satellite imagery and multiple remote sensing data to improve the flood inundation mapping technique in a data-scare South Asian watershed. The study considered the 2008 Bihar flood caused by the embankment breach of the Koshi River as a case study. This study used Landsat satellite's surface reflectance data to map the flood inundation using the commonly used water index known as the Modified Normalized Difference Water Index (MNDWI). MNDWI is a commonly used water classification technique to detect open surface water features using surface reflectance data sensed by the satellite. Further, the Normalized Difference Vegetation Index (NDVI), permanent water bodies, and Height Above the Nearest Drainage (HAND) datasets are used to mask the MNDWI map (initial flood inundation map) and improve the accuracy of the inundation map. In addition, different thresholding values of the final masked MNDWI map are applied to obtain more accurate and robust flood inundation maps with fewer false positive and false negative pixels.