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Research Progress on Ocean Observations Technology and Information Systems
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Research Progress on Ocean Observations Technology and Information Systems

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 8333

Special Issue Editors

Dr. Christos Tsabaris

E-Mail Website1 Website2
Guest Editor
Institute of Oceanography, Hellenic Centre for Marine Research, Athens, Greece
Interests: subsea in situ sensors; smart sensors; marine radioactivity; sediment dynamics; operational oceanography; radio-tracers; radio-protection; radioecology; rainfall; trace metals; climate change; natural hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriele Pieri

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, Pisa, Italy
Interests: Image processing for marine environment; multi-source data fusion; environmental decision support systems; marine information systems; machine learning methods; multimedia data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The oceans play a crucial role in the global ecosystem for shaping climate and weather trends, water management and health, and the biogeochemical cycles, representing valuable sources of oil, food, minerals and renewable energy. Although increasingly mature marine observation technologies have been developed during recent decades for a better understanding of the oceans, the new advances of existing operational observing systems have been constrained by limited cooperation and interaction between the managers of existing ocean networks on earth as well as between the observing units. Furthermore, the development of smart in situ marine sensors to be integrated into existing fixed units (such as landers and mooring buoys) as well as in mobile units (such as AUVs, ROVs, ships of opportunity, marine drones, Argo floats and gliders) are under development in the frame of various European and international projects. In recent years, a lot of progress has been made for the Ocean Observation Technologies and Information Systems by developing cost effective and miniaturized sensing devices with very low power consumption that would be directly integrated as a “plug and play” operational mode in existing sensor networks. Additionally, a lot of effort has been made to develop acoustic communication methods and modules to transmit the data from the deep ocean as well as cellular systems for transmitting the data of the marine sensors in near real-time mode using 4G/5G protocols (especially in coastal areas). In order to improve the processes that take place globally in the oceans (such as weather monitoring and forecasting, climate variability, sea level rise, natural hazards, ocean acidification, health of the ocean, pollution and ecosystem functioning, energy, economic development and coastal management, public safety, security, training and education), new research is ongoing to improve and optimize infrastructures and updated models at the international level. The state-of-the-art in situ marine sensors with the capability to be easily integrated into ocean platforms combined with innovative communication and information systems for real-time data transmission will emerge as new features of both forecasting methods and smart emergency systems to protect humans.

Dr. Christos Tsabaris
Dr. Gabriele Pieri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Ocean observation systems, fixed platform, mobile platforms, sensors’ integration, ocean networks, Marine Information Systems; environmental monitoring; maritime data processing and modelling

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Published Papers (6 papers)

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Research

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18 pages, 3633 KiB  
Article
Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets
by Zacharias Kapelonis, Georgios Chatzigeorgiou, Manolis Ntoumas, Panos Grigoriou, Manos Pettas, Spyros Michelinakis, Ricardo Correia, Catarina Rasquilha Lemos, Luis Menezes Pinheiro, Caio Lomba, João Fortuna, Rui Loureiro, André Santos and Eva Chatzinikolaou
J. Mar. Sci. Eng. 2025, 13(3), 611; https://doi.org/10.3390/jmse13030611 - 19 Mar 2025
Viewed by 388
Abstract
Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies [...] Read more.
Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies have far better propagation properties compared to optics; therefore, ASVs equipped with multibeam echosounders (MBES) are better-suited for mapping applications in deeper waters. In this work, a sea-bottom classification methodology is presented for mapping the protected habitat of Mediterranean seagrass Posidonia oceanica (habitat code 1120) in a coastal subregion of Heraklion (Crete, Greece). The methodology implements a machine learning scheme, where knowledge obtained from UAV imagery is embedded (through training) into a classifier that utilizes acoustic backscatter intensity and features derived from the MBES data provided by an ASV. Accuracy and precision scores of greater than 85% compared with visual census ground-truth data for both optical and acoustic classifiers indicate that this hybrid mapping approach is promising to mitigate the depth-induced bias in UAV-only models. The latter is especially interesting in cases where the studied habitat boundaries extend beyond depths that can be studied via aerial devices’ optics, as is the case with P. oceanica meadows. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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13 pages, 2203 KiB  
Article
The Integration of a Medium-Resolution Underwater Radioactivity System in the COSYNA Observing System at Helgoland Island, Germany
by Christos Tsabaris, Stylianos Alexakis, Miriam Lienkämper, Max Schwanitz, Markus Brand, Manolis Ntoumas, Dionisis L. Patiris, Effrosyni G. Androulakaki and Philipp Fischer
J. Mar. Sci. Eng. 2025, 13(3), 516; https://doi.org/10.3390/jmse13030516 - 6 Mar 2025
Viewed by 616
Abstract
The continuous monitoring of radioactivity in a cabled subsea network in the North Sea Observatory was performed to test the performance of a medium-resolution underwater spectrometer, as well as to identify and to assess potential anthropogenic and/or natural hazards. The effectiveness of continuous [...] Read more.
The continuous monitoring of radioactivity in a cabled subsea network in the North Sea Observatory was performed to test the performance of a medium-resolution underwater spectrometer, as well as to identify and to assess potential anthropogenic and/or natural hazards. The effectiveness of continuous monitoring was tested together with the operability of the underwater sensor, and quantification methods were optimized to identify the type of radioactivity as well as the activity concentration of radionuclides in the seawater. In the frame of the RADCONNECT project, a medium-resolution underwater radioactivity system named GeoMAREA was integrated into an existing cabled ocean observatory placed on Helgoland Island (COSYNA network). The system could be operated via an online mode controlled by the operational centre (AWI), as well as remotely by the end-user (HCMR). The system provided gamma-ray spectra and activity concentrations of key radionuclides that were enriched in seawater during the monitoring period. As concerns the quantification method of natural radioactivity, the average activity concentrations (in terms of the total monitoring period) of 214Bi, 208Tl, 228Ac and 40K were found to be 108 ± 30, 57 ± 14, 40 ± 5 and 9800 ± 500 Bqm−3, respectively. As concerns the quantification of 137Cs, the average activity concentration in terms of the total monitoring period (although it is uncertain) was found to be 6 ± 4 Bqm−3. The data analysis proved that the system had a stable operation in terms of voltage stability, so all acquired spectra could be summed up efficiently in time to produce statistically optimal gamma-ray spectra for further analysis. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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22 pages, 12425 KiB  
Article
Sea Clutter Suppression Method Based on Ocean Dynamics Using the WRF Model
by Guigeng Li, Zhaoqiang Wei, Yujie Chen, Xiaoxia Meng and Hao Zhang
J. Mar. Sci. Eng. 2025, 13(2), 224; https://doi.org/10.3390/jmse13020224 - 25 Jan 2025
Viewed by 474
Abstract
Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification. To address the challenge of existing filter parameters being unable to adapt in real-time to the characteristics of sea clutter, this paper [...] Read more.
Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification. To address the challenge of existing filter parameters being unable to adapt in real-time to the characteristics of sea clutter, this paper integrates ocean numerical models into the sea clutter spectrum estimation. By adjusting filter parameters based on the spectral characteristics of sea clutter, the accurate suppression of sea clutter is achieved. In this paper, the Weather Research and Forecasting (WRF) model is employed to simulate the ocean dynamic parameters within the radar detection area. Hydrological data are utilized to calibrate the parameterization scheme of the WRF model. Based on the simulated ocean dynamic parameters, empirical formulas are used to calculate the sea clutter spectrum. The filter coefficients are updated in real-time using the sea clutter spectral parameters, enabling precise suppression of sea clutter. The suppression algorithm is validated using X-band radar-measured sea clutter data, demonstrating an improvement factor of 17.22 after sea clutter suppression. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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16 pages, 4937 KiB  
Article
Improved Hierarchical Temporal Memory for Online Prediction of Ocean Time Series Data
by Tianao Qin, Ruixin Chen, Rufu Qin and Yang Yu
J. Mar. Sci. Eng. 2024, 12(4), 574; https://doi.org/10.3390/jmse12040574 - 28 Mar 2024
Cited by 2 | Viewed by 1620
Abstract
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped [...] Read more.
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped for the complexity of long-term marine time series applications, this study proposes a new, improved HTM model, incorporating Gated Recurrent Units (GRUs) neurons into the temporal memory algorithm to overcome this limitation. The capacities and advantages of the proposed model were tested and evaluated on time series data collected from the Xiaoqushan Seafloor Observatory in the East China Sea. The improved HTM model both outperforms the original one in short-term and long-term predictions and presents results with lower errors and better model stability than the GRU model, which is proficient in long-term predictions. The findings allow for the conclusion that the mechanism of online learning has certain advantages in predicting ocean observation data. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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15 pages, 4857 KiB  
Article
Argo Buoy Trajectory Prediction: Multi-Scale Ocean Driving Factors and Time–Space Attention Mechanism
by Pengfei Ning, Dianjun Zhang, Xuefeng Zhang, Jianhui Zhang, Yulong Liu, Xiaoyi Jiang and Yansheng Zhang
J. Mar. Sci. Eng. 2024, 12(2), 323; https://doi.org/10.3390/jmse12020323 - 13 Feb 2024
Viewed by 1917
Abstract
The Array for Real-time Geostrophic Oceanography (Argo) program provides valuable data for maritime research and rescue operations. This paper is based on Argo historical and satellite observations, and inverted sea surface and submarine drift trajectories. A neural network method was developed to predict [...] Read more.
The Array for Real-time Geostrophic Oceanography (Argo) program provides valuable data for maritime research and rescue operations. This paper is based on Argo historical and satellite observations, and inverted sea surface and submarine drift trajectories. A neural network method was developed to predict the position of Argo buoys, improving target tracking and emergency support capabilities. Based on a deep learning framework using a Simple Recurrent Unit (SRU), a new Time–Space Feature Fusion Method based on an Attention Mechanism (TSFFAM) model was constructed. The TSFFAM mechanism can predict the target trajectory more accurately, avoiding the disadvantages of traditional Long Short-Term Memory (LSTM) models, which are time consuming and difficult to train. The TSFFAM model is able to better capture multi-scale ocean factors, leading to more accurate and efficient buoy trajectory predictions. In addition, it aims to shed light on the mechanism of the joint multi-element and multi-scale effects of laminar and surface currents on multi-scale ocean factors, thereby deepening our understanding of the multi-element and multi-scale interactions in different spatio-temporal regimes of the ocean. Experimental verification was conducted in the Pacific Ocean using buoy trajectory data, and the experimental results showed that the buoy trajectory prediction models proposed in this paper can achieve high prediction accuracy, with the TSFFAM model improving the accuracy rate by approximately 20%. This research holds significant practical value for the field of maritime studies, precise rescue operations, and efficient target tracking. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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Review

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18 pages, 3334 KiB  
Review
Technology Review of Cabled Ocean Observatories
by Chang Shu, Feng Lyu, Rendong Xu, Xichen Wang and Wei Wei
J. Mar. Sci. Eng. 2023, 11(11), 2074; https://doi.org/10.3390/jmse11112074 - 30 Oct 2023
Cited by 1 | Viewed by 1967
Abstract
Cabled ocean observatories (COOs) have enabled real-time in situ ocean observations for decades, thereby facilitating oceanic understanding and exploration. This review discusses typical COOs worldwide in terms of system configurations and state-of-the-art technology, including network structures, power supply modes, and communication capabilities, and [...] Read more.
Cabled ocean observatories (COOs) have enabled real-time in situ ocean observations for decades, thereby facilitating oceanic understanding and exploration. This review discusses typical COOs worldwide in terms of system configurations and state-of-the-art technology, including network structures, power supply modes, and communication capabilities, and provides a comprehensive analysis of their technical routes. The main characteristics of line, ring, star, and grid networks and their applicability in COOs are elucidated, and the advantages and disadvantages of various power supply modes, as well as the opportunities brought by the development of communication technologies, are described. The insights gained from these discussions can inform the implementation of grid structures, optimization of cable routings, expansion of COO scales, application of dual-conductor submarine cables, and upgrading of communication capacity. On this basis, the challenges and future research directions related to COOs are presented. Full article
(This article belongs to the Special Issue Research Progress on Ocean Observations Technology and Information Systems)
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