Satellite-derived bathymetry (SDB) based on multispectral satellite images (MSI) from the satellite’s optical sensors is a recent technique for surveying shallow waters. Sentinel-2 satellite mission with an open access policy and high spatial, radiometric, and temporal resolution of MSI-s started a new era in the mapping of coastal bathymetry. More than 90 percent of the electromagnetic (EM) signal received by satellites is due to the atmospheric path of the EM signal. While Sentinel-2 MSI Level 1C provides top-of-atmosphere reflectance, Level 2A provides bottom-of-atmosphere reflectance. The European Space Agency applies the Sen2Cor algorithm for atmospheric correction (AC) to model the atmospheric path of the signal and reduce the MSI reflectance from L1C to L2A over the land area. This research evaluated the performance of different image-based AC processors, namely: Sen2Cor, Acolite, C2RCC, and iCOR for SDB modelling. The empirical log band ratio algorithm was applied to a time series of Sentinel-2 MSI in the middle Adriatic. All AC processors outperformed Sentinel-L2A MSI for SDB. Acolite and iCOR demonstrated accurate performance with a correlation coefficient higher than 90 percent and the RMSE under 2 m for depths up to 20 m. C2RCC produced more robust bathymetry models and was able to retrieve the depth information from more scenes than any other correction. Furthermore, a switch model combining different spectral bands improved mapping in shallow waters, demonstrating the potential of SDB technology for the effective mapping of shallow waters.

Advance in the visualization of the bathymetric and geological data from charted to digital maps and models opened the possibility to analyse data within Geographic Information System (GIS) functionalities. In this paper, bathymetric and geological properties of the Adriatic Sea were analysed using the General Bathymetric Chart of the Ocean (GEBCO) 2020 digital bathymetric model (DBM) and data from the European Marine Observation and Data Network (EMODnet). The bathymetric analysis includes depth statistics, area and volume calculation, hypsometry, and analysis of the heterogeneity of bathymetric data from the GEBCO 2020 DBM within the limits of the Adriatic defined by the International Hydrographic Organization (IHO) and 3 sub-basins delineated according to the bathymetry. The geological analysis includes seabed substrate map from EMODnet data and kilometre-scale seabed variability in the Adriatic. The GEBCO 2020 DBM shows that the Adriatic Sea is a shallow sea with a mean depth of -253 metres and over 50% of area shallower than 100 metres. The area of the Adriatic Sea is 138 516 km2 with a total volume of 35 521 km3. Patterns describing morphological variability coincide with the heterogeneity of the underlying source data of the GEBCO 2020 digital bathymetry model and major structures in the Adriatic Sea.

More than two-thirds of the Earth’s surface is covered by oceans and yet only a small portion of these oceans has been directly explored in detail, highlighting the need for powerful tools like remote sensing (RS) technology to bridge this gap. International frameworks, the 2030 Agenda for Sustainable Development, and Ocean Decade point out the significance of marine areas for achieving sustainable growth. This study conducts a bibliometric analysis of RS over marine areas for sustainable development to identify key contributors, collaboration networks, and evolving research themes from the beginning of the 21st century until last year. Using the Web of Science Core Collection database, 499 relevant articles published between 2000 and 2023 were identified. The bibliometric analysis showed a significant increase in scientific productivity related to the field. On an international level, China emerges as the most productive country, but international collaboration has played a crucial role, with 36.87% of articles resulting from international co-authorship, pointing to the global nature of research in this field. RS technology has continuously evolved from airborne sensors to the augmentation of Earth Observation missions. Our findings reveal a shift towards automated analysis and processing of RS data using machine learning techniques to integrate large datasets and develop robust scientific solutions.

Bathymetry underpins all marine and ocean research. It is common knowledge that there is a global deficit of high-resolution bathymetry based on modern acoustic techniques. Satellite altimetry enabled modeling of the global seafloor topography and revealed new morphological features in the unmapped areas of the oceans and seas. This chapter gives an overview of the physical problem and different approaches to estimating the bathymetry from satellite altimeter-derived gravity data. Characteristics of recent versions of frequently used global bathymetry models are presented. Moreover, this chapter demonstrates the possibility of regional bathymetry modeling by the gravity-geologic method in the Adriatic Sea.

Bathymetric models representing the topography of the seafloor are an important parameter in almost all maritime related research. Traditional bathymetric shipborne or airborne surveys are cost and/or time consuming, and access to the measured data is mostly limited or expensive. Alternative bathymetric data sources for marine researchers are publicly available bathymetric models whose quality is often unknown and/or uneven. This research presents the study on the bathymetric prediction for the Adriatic Sea from altimetry-derived gravity anomalies and in-situ soundings using the gravity - geologic method (GGM). Bathymetric soundings used to determine the density contrast between seawater and bedrock were derived from nautical charts, EMODnet (European Marine Observation and Data Network) bathymetric grid, and GEBCO (General Bathymetric Chart of the Oceans) One Minute grid. More than 3000 chart soundings distributed across the Adriatic Sea were used to estimate the quality of the predicted bathymetric model as well as the quality of the latest versions of publicly available bathymetric models: DTU10Bat (Technical University of Denmark), GEBCO 2020, EMODnet 2018, ETOPO1, Smith and Sandwell v.19.1, and SRTM (Shuttle Radar Topography Mission) 15+ V.2.1. The results show that the computed model represents an update to bathymetric data in the Adriatic Sea, especially along its eastern coast.