A technical and scientific overview regarding satellite-derived bathymetry (SDB)—one of the most promising and relatively cheap methods of shallow water depth determination—is presented. The main goal of the article is to present information about the possibilities of the SDB method to meet the demanding standard of bathymetric measurements in coastal mapping areas up to 20 m deep, i.e., up to depth areas where the largest number of ports and access waterways are located, as obtained using the bibliometric analysis. The Web of Science (WoS) and Scopus scientific databases, as well as R studio applications Bibliometrix and Biblioshiny, were used for scientific analysis. The bibliometric analysis presents the quantitative aspects of producing and disseminating scientific and professional articles with SDB as their topic. Therefore, the purpose of this study was to give the academic community an insight into the current knowledge about the SDB method, its achievements and shortcomings. The results of the bibliometric analysis of articles dealing with SDB show that most authors use empirical statistical methods. However, in recent years, articles using automated artificial intelligence methods have prevailed, especially the machine learning method. It is concluded that SDB data can become a very important low-cost source of bathymetric data in shallow coastal areas. Satellite methods have been proven to be very effective in very shallow coastal areas (up to a depth of about 20 m), and their biggest advantage is that the depth data obtained in this way are relatively low cost, while major limitations are associated with the parameters that determine the properties of the atmosphere and water column (clear atmosphere and water column) and bottom material. Procedures for different bathymetric applications are being developed. Regardless of the significant progress of the SDB method, which was manifested in the development of sensors and processing methods, its results still do not meet the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys S-44.

The paper gives a brief description of the remote sensing method used for the identification and extraction of water surfaces. Landsat 8 and Sentinel 2 satellite imagery was used to separate land from bodies of water in the complex karst area surrounding the Croatian Cetina River, flowing into the Adriatic Sea. Water indexing methods are presented in detail. The most frequently used water indices were selected: NDWI, MNDWI, AWEI_nsh, AWEI_sh, WRI and LSWI, and their results compared. The combination of satellite imagery and calculated water indices is concluded to be very useful for the identification and mapping of the area and banks of lakes, riverine zones, river mouths and the coastline in the coastal zone. Landsat 8 satellite imagery is slightly inferior to Sentinel 2 due to lower image resolution. The best results were obtained with the NDWI water index and the worst with LSWI.

The conditions for the occurrence of high waves in front of the Rijeka port in the Rijeka Bay were analyzed. The analysis was carried out on the basis of measured data on the wave rider station located in front of the main breakwater of the port of Rijeka and the results of numerical wave generation modelings for the wider sea area on the spatial scale of the Adriatic basin. The results of the conducted analysis show that the sudden transition in wind direction from the third to the second quadrant (and vice versa), with the simultaneous rapid increase in wind speed, creates the conditions for generating the largest waves in front of the port of Rijeka. The main reason for achieving the highest wave height in these conditions is the unbalanced wind power input with non-developed surface dissipation (white-capping) and quadruplet wave interaction. Situations with a slower increase in wind speed and approximately constant wind direction resulted in the occurrence of smaller wave heights. The direct application of anemometric data for the forcing wind field in the Adriatic basin within the wave generation model results in a more accurate simulation of wave height and wave period development than application of the wind field from the prediction atmospheric model Aladin-Hr. This is due to the fact that the site is located in a semi-enclosed sea area of restricted fetch, and the spatial/temporal resolution of atmospheric data (2 km and 3 h) is not sufficient to resolve the rapid transition in the wind field. In the case of direct application of anemometric data, the white-capping parameterization should be of a non-stationary character.