# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 1 Introduction
Hence, an automatic tool enabling continuous and systematic monitoring of water surfaces by routinely producing medium-high resolution maps of floodwater/permanent water can complement the rapid mapping component of CEMS for what concerns flood management. In particular, it can cope with the timeliness in the availability of maps because a continuous service does not require any user activation. Moreover, it can contribute to the effectiveness of CEMS activation through a better identification of the area of interest, when images with a very high spatial resolution are required for an accurate delineation of the flood extent. The regular acquisitions of Sentinel-1 (S1) images, performed in the framework of the Copernicus program, guarantee a continuous streaming of C-band SAR data useful to systematically produce maps of water surfaces at medium-high spatial resolution (S1 data over land have a single-look resolution of 5 \(\times\) 20 m\({}^{2}\), Section 2.1). The availability of S1 data represents therefore the driver for the implementation of the aforementioned tool. A systematic near real-time (NRT) surface water mapping service has been recently requested by the Italian Department of Civil Protection (DCP) in the framework of its competences assigned by law. This paper presents an automatic processor, which does not need any supervision, or user intervention, that performs a systematic daily mapping of water surfaces (both floodwater and permanent water bodies) using S1 data. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote this tool. AUTOWADE has been designed in the framework of the agreement between DCP and CIMA Research Foundation, which is one of the DCP competence centers. Therefore, it has been conceived to work on a national (Italian) scale. The implementation of a service for fast mapping of floods and inland waters using S1 data requires the design of a chain formed by different components. The main component is obviously water detection, but other components must be included,like the automatic downloading of S1 images and their preprocessing to derive geocoded and calibrated data, the generation of reference and exclusion masks, and the delivery of the data to the end user. All these components must be sequentially executed in a fully automated way. Despite the large number of studies dealing with NRT water mapping, the problem of the creation of a tool that automates the various steps needed to set up a daily service based on SAR data was rarely tackled in past studies. A fully automated TerraSAR-X based flood service was developed in [25] and adapted to S1 data in [28]. Although the present release of AUTOWADE focuses on open water (i.e., open floodwater and permanent water bodies), vegetated areas close to open water that are likely inundated as well are searched for, despite the criticalities previously underlined; the objective is to, at least partially, fill the gaps typically present in flood maps due to undetected flooded vegetation. Conversely, urban areas are not considered because the related methodologies require interferometric data (e.g., [8; 15; 16]), whose processing requires the use of S1 orbit ephemerides data, available 20 days after the data acquisition. The algorithms implemented in AUTOWADE use different image processing techniques. Flooded vegetation is detected through a combined use of co- and cross-polarized S1 data performed taking advantage of the fuzzy logic. Open water is mapped using a new methodology, denoted as buffer-from-edge (BFE) approach, which combines clustering, edge filtering, automatic thresholding, and region growing; it is applied to co-polarized data only. At present, a combined use of different image processing techniques such as image tiling [29], used to identify a threshold separating open water from the surrounding land, and region growing is well-established for water mapping from SAR (e.g., [18; 24; 26; 30; 31]).