## 1 Introduction  
Flood disasters are high-frequency events with a wide influence, causing serious impacts on ecological environments, human societies, and economies. Accurately and quickly evaluating the location, scope, and intensity of flood disasters is necessary for disaster analysis and rescue planning [1]. Disaster assessment is based on the inundation range of the flood disaster. As an advanced and widely used technology, remote sensing is eminently suitable for assessing flood coverage [2]. In remote-sensing images, floods are characterized by clear boundaries. Therefore, many recent studies have extracted flooded coverage from satellite data [3, 4]. Satellite-based remote sensing has become an important part of earth observation. Besides providing high spatial, spectral and temporal resolution, it has been developed on multiple platforms and sensors [5]. Among these developments, multispectral, thermal and radar data are the main supports for flood coverage extraction [6, 7]. Multispectral and thermal data, such as those drawn from Landsat and MODIS, are widely used and have resulted in a variety of products [8, 9]. For example, Landsat data were used to generate an analysis read dataset (ARD) using the dynamic surface water extent (DSWE) algorithm, developed by the United States GeologicalSurvey (USGS), for the US and its territories [10], global surface water (GSW) [11], and the global 3-s/1-s water body map (G3 WBM/G1 WBM) [12]. Synthetic aperture radar (SAR), which is less susceptible to cloud interference than optical data, is promising as a means of monitoring the temporal and spatial changes in flood extent [13]. Three major development stages were enabled by using SAR data for flood monitoring. In the early era of flood analysis, coarse-resolution single-polarized SAR data were used to extract texture features and water-area backscatter for flood detection [14; 15]. Using SIR-C and 100-m resolution JERS-1 L-band data, [PERSON] et al. achieved a 62% user accuracy for flood detection in the Amazon River at low water levels and 86% at high water levels [16]. [PERSON] et al. [17] analyzed one multi-polarized ASAR data set, highlighting the greater capability of the Envisat ASAR instrument for flood mapping, particularly the benefits of combining similarity and cross-polarization for rapid mapping in crises. Recently, multi-temporal SAR data, with less than 10 m spatial resolution, provided the ability to monitor the flood event within hours of a disaster. [PERSON] et al. developed a real-time automatic flood detection tool using segmented histogram thresholding for high-resolution TerraSAR-X SAR data (3 m) [18]. [PERSON] et al. also focused on high-resolution TerraSAR-X data and proposed a semi-automatic algorithm for flood detection in urban areas [19; 20]. [PERSON] et al. tested and evaluated two case studies of the 2011 Richelieu River flood using the t-resolution data (3 m) [21]. [PERSON] et al. analyzed the flood situation after the 2011 tsunami that hit Japan, using COSMO-SkyMed data [22]. Multiple authors proposed methods for flood extraction using Sentinel-1 SAR data. [PERSON] et al. presented an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT) [23]. Indeed, super-high-resolution sensors, such as ICEYE, could collect worldwide daily SAR data at 0.25 m spatial resolution for ice management use [24]. However, to date, the ICEYE data has not been freely available. Flood coverage extraction from radar data has gradually increased over the years, and various extraction methods have become available [25; 26]. On the one hand, thresholding is one of the most widely used approaches to delineating water extent because of its effectiveness and efficiency [27]. In digital image processing, thresholding is the simplest method of segmenting images. Currently, the most commonly used segmenting images methods include superpixel segmentation methods [28; 29], watershed segmentation methods [30; 31], and level set segmentation methods [32; 33].