# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 1 Introduction  
Multispectral imagery provides an alternative way to acquire marine environmental data for several operational, as well as scientific, purposes [1; 2; 3; 4], in different time and spatial scales. Satellite imagery can provide users with adequate coverage and often free-of-charge environmental information, like those acquired by the Landsat 8 (OLI) and Sentinel-2 sensors [5]. Currently, the acquisition of spaceborne observations is a common practice, however, the processing of imagery data to extract bathymetry is a more complex procedure, since it requires special treatment for estimating the subsurface irradiance reflectance signal (\(R(0-)\)). In general, the range of extracted depths is strongly related to the water clarity, or the so-called ocean color (IOPs, turbidity, etc.) The main issue is that the ocean color depends directly on the optical properties of the water, which in turn vary spatially and temporally [6]. As a result, a special methodology is needed for correcting multispectral images due to atmospheric and water column interactions.  
After pre-processing the imagery data with respect to atmospheric interference (i.e., via models such as 6 SV, OPERA, etc.) [7; 8], and air-water surface interaction, the remaining signal propagatingfrom the seab can provide the bathymic information. Then, empirical or analytical-based inversion models are applied for obtaining satellite-derived bathymetry (SDB) [9; 10].  
By implementing field data for training and vertical calibration of empirical algorithms, SDB modelling looks to be an effective procedure to acquire depth information in shallow waters [11]. Several algorithms and techniques have been proposed, attempting to calculate water depth in order to then implement it as a fusion variable for estimating other bio-optical environmental parameters. Researchers also tested machine learning methods for extracting water depths down to 20 m in high-transparency coastal areas and with multi-temporal and spectral images [12].  
Shallow seabed bathymetry is a major component of the hydrographic surveying and nautical chart production. Today, charts are produced from Lidar and/ or sonar (single or multi-beam). Although the bathymetric measurements collected using acoustic systems are more accurate than those extracted from satellite imagery, surveying vessels cannot move freely and without risk in shallow waters, mainly due to their draft and length limitations. In addition, vessel deployment is a costly and time-consuming activity. On the other hand, SDB can provide surveyors with topographic data (e.g., coast lining and positioning of conspicuous points), as well as bathymetry, bentnos, and seabed-texture; these are all fundamental topological features that can assist environmental monitoring and be used in marine cartography. Many scientists indicated the lack of up-to-date data over large coastal regions of dynamically changing bathymetry (e.g., river mouths); these bathymetric gaps may be treated with fast and reliable SDB data, in conjunction with satellite positioning systems (GNSS) and satellite communication services [3]. For the above-mentioned reasons, hydrographers and cartographers strive to discover ways to minimize manpower in order to collect depth observations, which are in line with the standards of both the International Hydrographic Organization (IHO) and the International Maritime Organization (IMO), related to navigation safety [11].  
Hence, the aim of this research is to develop a new SDB method to be used for rapid production and updating of nautical charts, and to fulfil the requirements of other marine applications related to coastal management and monitoring.