Abstract
Space-based synthetic aperture radar (SAR) is a powerful tool for monitoring flood conditions over large areas without the influence of clouds and daylight. Permanent water surfaces can be excluded by comparing SAR images with pre-flood images, but fluctuating water surfaces, such as those found in flat wetlands, introduce uncertainty into flood mapping results. In order to reduce this uncertainty, a simple method called Normalized Backscatter Amplitude Difference Index (NoBADI) is proposed in this study. The NoBADI is calculated from a post-flood SAR image of backscatter amplitude and multiple images on non-flooding conditions. Preliminary analysis conducted in the US state of Florida, which was affected by Hurricane Irma in September 2017, shows that surfaces frequently covered by water (more than 20% of available data) have been successfully excluded by means of C-/L-band SAR (HH, HV, VV, and VH polarizations). Although a simple comparison of pre-flood and post-flood images is greatly affected by the spatial distribution of the water surface in the pre-flood image, the NoBADI method reduces the uncertainty of the reference water surface. This advantage will contribute in making quicker decisions during crisis management.
Highlights
Serious flooding events are caused by extreme weather conditions and seasonal tropical cyclones, and it is important for affected societies to respond quickly to disasters based on spatial awareness of the observed flood extent
This study proposes Normalized Backscatter Amplitude Difference Index (NoBADI), which expresses how rare the water cover is in each pixel
Calculate NoBADI with the following equation: This study proposes NoBADI, which expresses how rare the water cover is in each pixel
Summary
Serious flooding events are caused by extreme weather conditions and seasonal tropical cyclones, and it is important for affected societies to respond quickly to disasters based on spatial awareness of the observed flood extent. Space-borne Synthetic Aperture Radar (SAR), a remote sensing technology from space, is an advanced solution for monitoring large-scale flood disasters extensively and quickly that is not hampered by cloud cover. The easiest method for identifying a flood surface is to extract low backscatter pixels, which in this case includes the permanent water surface. To exclude the permanent water surfaces such as rivers and lakes, significant backscatter reduction from a pre-flood SAR image would be extracted as flood-derived water surfaces [1,2,3,4]. Advanced methods analyze phase information based on the interferometric SAR (InSAR) technology.
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