Automatic Extraction of Flood Coverage Based on Dynamic Surface Water Extent and SAR Data

Abstract

Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. A large number of researches utilized optical remote sensing data to extract surface water extent. However, current data products derived from optical sensors are difficult to meet the need of rapid flood monitoring due to cloud cover. Radar remote sensing supports ‘all-weather’ and ‘day-and-night’ water information extraction. Here, we proposed an automatic thresholding approach to extract flood coverage using Sentinel-1 synthetic aperture radar (SAR) and prior classes information. Sixteen years of Landsat remote sensing image data were analyzed by Google earth engine (GEE) and prior classes of water and non-water were generated from composited dynamic water extent (cDSWE). We combined the distribution probability data of the prior classes of water with the extracting result to calculate the area of inundation. Two sites, Shouguang in Shandong province and Ji'an in Jiangxi province, were selected as representative areas for reservoir and floodplain. The results show that the overall classification accuracy for water is above 90%. Commission errors of water bodies ranged from 0% to 11.32%, and the omission errors ranged from 6.0% to 10.2%. Validations indicate the algorithm can effectively extract the spatial extent of reservoir or floodplain.