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: 10 February 2025 | Viewed by 6241

Special Issue Editors


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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
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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

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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 (4 papers)

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Research

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22 pages, 12417 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 239
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
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 1381
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
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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
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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
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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 1765
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
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The integration of a medium resolution underwater radioactivity system in the COSYNA observing system at the Helgoland Island, Germany
Authors: Christos Tsabaris; Stylianos Alexakis; Miriam Lienkämper; Markus Brand; Dionysis L. Patiris; Manolis Ntoumas; Philipp Fischer2
Affiliation: Hellenic center for Marine Research, Institute of Oceanography; Alfred-Wegener-Institute for Polar and Marine Research, Centre for Scientific Diving ###co-authored with AWI###
Abstract: The continuous monitoring of radioactivity in the oceans is crucial to identify potential incidences due to accident or blasts as well as due to various phenomena that may take place due to climate change and natural hazards. Effective monitoring provided in a continuous basis the type of the radioactive contaminant as well as the activity concentration of radionuclides in different matrices in the ocean environment. The spatial and temporal monitoring is performed using in-situ methods combined with low consumption detection systems that are enough tolerant for ocean conditions, while the data transmission is performed using wireless or on line methods. In the frame of RADCONNECT project, the integration of a medium resolution underwater radioactivity system was integrated in an existing cabled ocean observatory placed in Helgoland Island. The system operated in an online mode controlled by the operational Centre (AWI) as well as from the end-user (HCMR). The system provided gamma-ray spectra and activity concentration of key radionuclides that were enriched in the sediment as well as in the seawater during the monitoring period. The data analysis proved that the system had a stable operation in terms of voltage stability so that all acquired spectra could be integrated effectively in time to produce high statistically gamma-ray spectra for further analysis.

Title: Using a new generation design Passive Aquatic Listener - Spotter for the Quantifying Physical Processes in the Marine Environment
Authors: Panagiotis Mitsopoulos, Marios N. Anagnostou*, Michalis Kyriakou, Andreas Koras, Pena Mendez Malaquias, and Emmanouil Anagnostou
Affiliation: Ionian University, Department of Informatics,
Abstract: the marine environment and the oceans are exposed to many physical processes including the continuously increased noise/sound levels and transients created from human activities. Some of these anthropogenic sound sources are shipping and the building of foundations for offshore construction projects (such as oil drilling and windfarms). A lot of effort is given to study the impact of the anthropogenic noise/sound levels on species that inhabit the marine environment. Underwater noise is an important aspect of the Marine Strategy Framework Directive (MSFD), which aims to achieve good environmental status (GES) of the European marine environment. Noise has been defined by the MSFD as sound that causes negative effects to the marine life or as a pollutant to the ocean environment and it focuses on energy inputs for ocean noise from underwater sound. These sounds are often recognizable by their spectral or temporal characteristics. By training an underwater passive recorder, named Passive Aquatic Listener (PAL) to listen and classify the sound source and then quantify them to physical variables, can be made in real time, and reported as interpreted data, rather than just sound levels. These measured sounds are interpreted as, for example, wind speed, precipitation, sea ice type and flow rate. The PAL and SOFAR's Spotter buoy integrated for a first time to form a real-time data transmitter system to monitor these physical environmental variables. The Spotter is a solar powered surface buoy with multi-sensor and data transmission capabilities. The novelty of this coupled hybrid system relies upon combining a low-cost, state-of-the-art passive acoustic sensor with near real‐time data processing and transmission while providing deployment flexibility. Furthermore, the system's low power consumption and maintenance allow long-term monitoring. The underwater acoustic data are sent to the surface through SOFAR's Smart Mooring line. The detected and classified acoustic data are transmitted along with the Spotter's oceanographic observations in real-time to SOFAR's API via a cellular network. The PAL-Spotter system and its capabilities and data are intended to supplement observational information to the Southern New England region's offshore wind farm construction operations. Indicatively, this work demonstrates that remote acoustic monitoring of the ocean environment will produce quantitative measurements of physical processes that will be continuous, in near real time, and from times and locations where it is difficult or impossible to make such measurements by more conventional methods.

Title: Flying robots teach floating robots – A machine learning approach for marine habitat mapping based on combined datasets
Author: Chatzinikolaou
Highlights: • Unmanned aerial (UAVs) and surface (USVs) vehicles can be used for the study of coastal and marine environments • UAV photogrammetry reveals the sea bottom composition in high quality in shallow waters • Acoustic methodologies such as USVs equipped with multibeam echosounders have better propagation properties in deeper waters.

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