## 1 Introduction  
Flash floods are considered to be among the most frequent and destructive types of natural disasters worldwide, with significant consequences including: (a) human and animal life losses, (b) agricultural crop destruction and soil loss, (c) damage to infrastructure and communication networks, and (d) transport of sediment loads and pollutants [1; 2; 3; 4; 5; 6; 7; 8; 9]. Particularly, it is estimated that floods provoke about 40% of the damage caused by all natural disasters [10; 11]. Definitions and classifications vary for different types of floods, including catastrophic, flash, extreme, river, coastal, pluvial, and large floods [12; 13; 14]. Flash floods are the most common type of flood in the Mediterranean areas, which occur due to the small size of the river basins (short flow concentration time), the geomorphology (high slopes or/and low soil and geological formations permeability), and intense rainfall [15]. Therefore, mapping and monitoring flood events and their impacts are essential for any flood risk mitigation plan, disaster detection, and compensation services [16]. This information is often difficult to produce using traditional techniques because inundated areas are transient, changing fast, cover large areas, and by their nature restrict the accessibility [17]. Earth observation (EO) data from space can provide valuable and timely information aiding the effective response and mitigation of natural hazards such as floods. Satellite observations enable the acquisition of data for large and hard-to-reach areas, they may provide continuous measurements, and facilitate the detection of the flood extent, which is crucial information for damage assessment and risk management [7; 9; 18; 19]. Currently, the most common approach is to use both optical and radar data. Satellite images captured by optical sensors, such as those on board the Landsat-7 (L7), or Landsat -8 and Sentinel-2 (S2)satellites; these satellites acquire data from several spectral bands that cover the visible through shortwave infrared (SWIR) spectrum, and provide high spectral resolution and temporal data as well as a substantial archive to cover a range of hydrological conditions [20; 21]. Optical sensors detect energy naturally reflected or emitted by the earth's surface in visible and infrared spectral bands. Where clouds, trees, and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas [18; 22; 23]. Several different water indices have been used up to now to delineate flooded areas in studies using Landsat imagery. Specifically, the Normalized Difference Water Index (NDWI), which can outline and enhance open water features by means of near infrared (NIR) and green channels of Landsat, was initially used [24; 25]; it was later modified (Modified Normalized Difference Water Index) by substituting the middle infrared band for the NIR band [26; 27; 28]. The MNDWI can enhance open water features while efficiently suppressing and even removing built-up land noise as well as vegetation and soil noise [26]. Tasseled cap transformations (TCT) of L7 ETM+ data have also been used to map water [29; 30; 31]. TCT summarizes Landsat data into fewer components, by rotating pixel vectors in multidimensional space, using all bands. The third component of the L7 tasseled cap transformation relates to wetness, and tasseled cap wetness (TCTW) has been used as an input to delineate the flooded area [32; 33; 34]. Furthermore, several other differentiated and sophisticated water indices have been more frequently utilized, such as the Normalized Difference Water Index (NDWI) of McFeeters, the Difference Between Vegetation and Water Index (DVW), the Index of Free Water (IFW), the Water Impoundment Index (WII), the Modified Water Index (MWI), the Red and Short Wave Infrared index (RSWIR), and many others [24; 26; 27; 34; 35; 36; 37; 38; 39].