# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 1 Introduction  
Floods are the most frequent and costly natural hazard worldwide, causing fatalities and significant economic damage in both rural and urban environments [1]. According to the Emergency Events Database, floods were the most common natural hazards in 2022, with 176 large events occurring worldwide, approximately 8000 deaths, and more than 57 million people affected [2]. Unfortunately, global temperature increases are generating widespread shifts in weather systems, making events such as floods more frequent, intense, and unpredictable [3]. The timely detection and monitoring of flood-prone areas are crucial for effective disaster management and response [4]. In recent years, remote sensing (RS) technologies have emerged as powerful tools for flood monitoring, with Synthetic Aperture Radar (SAR) at the forefront [5]. SAR imagery has indeed proven to be a powerful and effective approach for flood mapping [6; 7; 8]. Unlike optical sensors, SAR can penetrate clouds, rain, and vegetation, providing all-weather and all-day imaging capabilities [9], thus allowing for the detectionand characterization (extent, dynamics, and severity) of flooded areas at high spatial resolution [10; 11; 12; 13]. The main limitation, especially in the past, of using SAR data for flood detection and monitoring purposes, namely their low temporal resolution, has been recently addressed by the implementation of several constellations of satellites sharing the same payload and orbit with a phase shift that allows for at least half of the temporal resolution when two satellites are present [14]. An example of such a constellation is represented by the Sentinel-1A (S-1A) and Sentinel-1B (S-1 AB) satellites, which belong to the European Space Agency (ESA) Copernicus program and are equipped with a SAR system operating in the C-band (5.6 GHz) [15]. Each SAR has a spatial resolution of 10 m, while the nominal temporal resolution of each of the two S1 satellites is 12 days, which is reduced to six days when the two satellites are working together. This resolution increases at higher latitudes and over Europe, which are sampled more frequently than other locations in the world [14]. Various approaches have been employed for flood mapping using SAR imagery [8; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. One commonly employed method is image differencing, which involves comparing pre- and post-flood SAR images to identify changes in the backscattering characteristics [12; 13; 16]. This approach was recently used by [PERSON] and [PERSON] [22], who analyzed multi-temporal Sentinel 1 data and highlighted the relevance of temporal analysis for effectively detecting and mapping floods. However, it is important to note that flood-affected pixels may exhibit signals similar to other features, such as high-altitude regions or shadows [18]. Thresholding is another approach in which a threshold value is set to distinguish between flooded and non-flooded areas based on the SAR image intensity values. Pixels above the threshold are classified as flooded, while those below are classified as non-flooded. This technique was recently applied to the historical Sentinel 1 data by [PERSON] et al. [23] for rapid flood mapping. However, this method has limitations when it comes to detecting water through dense vegetation [18]. Machine learning and data fusion techniques have been employed for flood mapping using Sentinel-1 data. [PERSON] et al. [24] used training algorithms to classify flooded and non-flooded areas based on a combination of SAR data, ancillary data, and historical flood information. Another study by [PERSON] et al. [25] presented a method for flood extent mapping using an ensemble CNN model, further illustrating the use of advanced machine-learning techniques for flood detection. However, training these models requires large amounts of labelled data, which can be difficult to obtain.