# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 1 Introduction
The first in situ inland excess water maps in Hungary date from the middle of the 20 th century; since then, observations have been carried out during every rainy period in areas affected by IEW. This approach is labor intensive and can easily lead to errors due to misinterpretation and differences in observation methodology. Aggregating the in situ maps over time can be useful to create maps showing the vulnerability to inland excess water floods at an approximate scale of 1:100,000. Monitoring is not possible using the field observations, since normally only the maximum observed inundation during an IEW period is drawn on a map. [PERSON] [11] was one of the first to perform hazard mapping based on factors causing inland excess water resulting in the national inland excess water index map. Since then, many national, regional and local versions of this approach have been published (e.g., [12; 13; 14; 15; 16; 17]). The maps provide information on the vulnerability of an area to IEW, but do not give information about actual occurrences, nor about the development of the phenomenon. Modelling of inland excess water has been performed using hydrological modelling packages as well [18; 19]. This approach can result in detailed models of the inundations, but requires large amounts of accurate input data, which is often only available for very small areas. The fourth approach to map and monitor IEW is based on remote sensing data and algorithms. Data collected from small (drones), medium (aerial photographs) and large (satellite imagery) areas have been used to detect inland excess water. The field of remote sensing provides a set of well-understood methods that can be applied in a standardized method allowing to create uniform IEW maps of large areas with good spatial resolution. Data from different passive sensors have been extensively used for this purpose, for example [20; 21; 22; 23; 24; 25; 26]. The disadvantage of passive data is their limited usability during bad weather conditions, which often occur during IEW periods. Therefore, approaches using active satellite data to detect IEW or other shallow temporal water bodies have been published as well [10; 23; 27; 28; 29; 30; 31; 32]. In general, radar data have been used for water and especially flood identification in many studies. For example, [PERSON] et al. [33] developed a method to detect open water bodiesusing a dual threshold method with high-resolution Radarsat 2 data. Also, approaches of combined monitoring of surface water with optical and radar data have appeared [34; 35]. With the development of ESA's Copernicus program, a large fleet of satellites has appeared that provides data for a large range of applications. The Sentinel-1 satellites have been among the first platforms in the program and provide data using an active remote sensing instrument. The data set is an improvement over earlier radar data sets because it provides continuous, near global data with a high temporal interval [36]. The Sentinel-2 satellites in the constellation are multispectral satellites with a high temporal interval as well. The combined use of Sentinel-1 and Sentinel-2 data has been studied for different applications, like land use/land cover classification [37; 38; 39] and wetlands mapping [40]. Although flood detection based on Sentinel-2 has been studied before [41; 42], a fully automated approach based on a combination of Sentinel-1 and -2 to determine the extent of non-permanent shallow water bodies has not been published to the knowledge of the authors. The aim of our research is to develop a methodology that is capable of continuously monitoring inland excess water over large areas to improve complex water management. Our research questions are (1) what combination of satellite data can be used for large-scale IEW mapping, (2) which methods can be fully automated to be able to create standardized, repeatable, weekly IEW maps and (3) what is the accuracy of the methodology?