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J. Zhao, R. Pelich, R. Hostache, P. Matgen, W. Wagner, M. Chini:
"A large-scale 2005-2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case";
Remote Sensing of Environment, 256 (2021), 112338; 1 - 18.

Synthetic Aperture Radars (SAR) are adequate sensors for mapping water bodies from space as they can be used to acquire data of equal quality both day and night and practically regardless of weather conditions. Furthermore, the global coverage of SAR data provides an opportunity to generate global scale flood records that are essential for improving our understanding of flood risks worldwide and of how these risks are changing over time. In this study, we introduce an automatic change-detection based method that allows global-scale flood records to be generated using the readily and freely available ENVISAT-ASAR data collection. It consists of the following three steps: (i) flood image identification; (ii) reference image selection; (iii) floodwater detection. As a test case, this study uses all available ENVISAT-ASAR images from eight different orbital tracks that were acquired over the United Kingdom over the period 2005-2012. Due to a lack of large-scale ground truth data, the evaluation of the results is carried out using different data sources. First, subsets of the flood maps over the Severn River basin are evaluated using a flood extent map that was manually digitized from very high-resolution aerial imagery. According to our results, the overall accuracy of both flood maps' subsets is higher than 85% while the user accuracy of the flood class is above 88%. Next, for the regions and images without available ground truth data, a visual inspection is carried out using simulations generated by the hydraulic model LISFLOOD-FP, as well as LANDSAT 7 ETM+ images obtained with a 30 m spatial resolution. Meanwhile, by comparing the acquisition dates of identified flood SAR images, the LISFLOOD-FP model results and optical data, a good agreement has been found. The experimental results over the United Kingdom indicate that the proposed method has strong potential for the generation of a global flood data record from the ENVISAT-ASAR archive.

ENVISAT, Global flood record, Fully automatic change detection, Flood image identification

http://dx.doi.org/10.1016/j.rse.2021.112338