A PRELIMINARY COMPARISON OF TWO EXCLUSION MAPS FOR LARGE-SCALE FLOOD MAPPING USING SENTINEL-1 DATA

Abstract

Due to its ability to acquire data regardless of weather conditions and solar illumination, Synthetic Aperture Radar (SAR) intensity data is the preferred data for large-scale flood mapping. However, due to the SAR image distortions and complex land cover conditions at large scale, there are areas where SAR data is unable to measure ground surface changes caused by floodwater, which is crucial information that cannot be overlooked for large-scale applications. To address this limitation of SAR data, two similar products, the LIST Exclusion map (EX-map) and the GFM exclusion mask, were recently proposed to identify these problematic areas. As there is no established criterion to evaluate these two products, a comprehensive comparison is necessary to investigate the consistency and differences between them for different end-users’ needs. We conducted the first-ever comparison between the LIST EX-map and the GFM exclusion mask, from their definitions to the site-scale products, while elaborating on their preferred application domains for different algorithms. We qualitatively and quantitatively evaluated the exclusion map using Sentinel-1 data for 11 test sites across five continents with global land cover maps to identify the advantages and disadvantages of both approaches. The results show that the main differences exist in mountainous radar layovers/shadows and low vegetation such as grass, cropland, and shrubland. The evaluation results demonstrate a good agreement (64.87% ∼ 91.40%) between the two products.