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Advances in Ocean Observing Technology and System
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Advances in Ocean Observing Technology and System

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: 31 May 2026 | Viewed by 1086

Special Issue Editor

Dr. A. Miguel P. Santos

E-Mail Website
Guest Editor
Portuguese Institute for the Sea and Atmosphere (IPMA), 1495-165 Lisbon, Portugal
Interests: observing systems; fisheries oceanography; physical-biological interactions; coastal upwelling; Argo; GOOS; MSFD; local community capacity building
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ocean plays a central role in regulating Earth’s climate, supporting biodiversity, and sustaining human societies. Yet, large portions of it, particularly the deep sea and remote regions, remain vastly undersampled. In recent decades, rapid technological advancements have transformed ocean observing, enabling a more comprehensive, high-resolution, and autonomous understanding of marine processes. The integration of in situ and remote sensing platforms, from satellite constellations to underwater gliders, Argo profiling floats, moored observatories, fishing vessels and gears, SMART cables, autonomous and uncrewed surface vehicles (A/USVs), has revolutionized the capacity to monitor the ocean’s physical, chemical, and biological dynamics continuously and in near real time. Emerging innovations such as biogeochemical sensors, cabled observatories, and smart subsea infrastructure are expanding the scope of sustained observation from the coastal zone to the deep sea. At the same time, advances in data processing, artificial intelligence, and digital twin technologies are accelerating the transformation of raw ocean data into actionable information for climate modelling, ecosystem assessment, hazard forecasting, and resource management. These developments are increasingly framed within global efforts, such as the UNDOS, GOOS, and regional research infrastructures like EMSO-ERIC, IOOS, IMOS, ICOS, ONC, and EPOS, to name a few. This Special Issue on “Advances in Ocean Observing Technology and System” aims to bring together recent scientific and technical contributions addressing the design, implementation, and application of innovative ocean observing and monitoring systems. Topics of interest include sensor development, autonomous platforms, data integration, multi-scale observing networks, and the role of public–private partnerships in sustaining ocean observations. Together, these advances support the transition toward a globally connected, interoperable, and adaptive ocean observing system that can meet the challenges of a changing planet.

Dr. A. Miguel P. Santos
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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 observing systems
  • autonomous platforms
  • marine sensors
  • observatories
  • Argo
  • glider
  • ocean data
  • artificial intelligence in oceanography
  • digital twins of the ocean
  • UN ocean decade
  • GOOS

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Published Papers (3 papers)

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Research

16 pages, 2164 KB  
Article
An Assessment of the Moana Operational Forecast System Assimilating Innovative Mangōpare Fishing Vessel Observations in Aotearoa, New Zealand
by Joao Marcos Azevedo Correia de Souza and Carine de Godoi Rezende Costa
J. Mar. Sci. Eng. 2026, 14(7), 591; https://doi.org/10.3390/jmse14070591 - 24 Mar 2026
Viewed by 196
Abstract
Coastal seas around Aotearoa, New Zealand, are among the least observed parts of the global ocean, limiting our ability to monitor and forecast marine conditions. The Moana Project addresses this gap with a new observing system that includes temperature sensors mounted on commercial [...] Read more.
Coastal seas around Aotearoa, New Zealand, are among the least observed parts of the global ocean, limiting our ability to monitor and forecast marine conditions. The Moana Project addresses this gap with a new observing system that includes temperature sensors mounted on commercial fishing gear—the Mangōpare fishing vessel network. This study presents the first evaluation of New Zealand’s operational ocean 4D-Var data assimilation system that incorporates these fishing vessel (FV) observations into a regional ROMS model. Using just over one year of operational forecasts, we show that FV temperature profiles significantly improve subsurface temperature representation, especially in coastal regions where satellite products have warm biases or miss key features such as upwelling and mesoscale variability. Assimilation of FV data reduces background temperature biases throughout the upper ocean and enhances forecast skill in areas influenced by major currents and dynamic coastal processes. We also identify sensitivity to periods of missing satellite sea surface temperature, which can lead to overfitting of the available observations. Overall, the results demonstrate that FV observations provide essential subsurface information and can substantially strengthen operational coastal ocean forecasting systems. Full article
(This article belongs to the Special Issue Advances in Ocean Observing Technology and System)
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14 pages, 2672 KB  
Article
In Situ Measurement of Oceanic 3D-Volume Two-Component Turbulence Based on Holographic Astigmatic Particle Tracking Velocimetry
by Zhou Zhou, Buyu Guo, Wensheng Jiang, Changwei Bian and Fangjing Deng
J. Mar. Sci. Eng. 2026, 14(6), 574; https://doi.org/10.3390/jmse14060574 - 19 Mar 2026
Viewed by 158
Abstract
Ocean turbulence, a fundamental process influencing marine hydrodynamics, holds significant guiding implications for the development of multiple disciplines and has emerged as a research hotspot in ocean science in recent years. However, constrained by traditional oceanographic instruments limited to single-point measurements, current observations [...] Read more.
Ocean turbulence, a fundamental process influencing marine hydrodynamics, holds significant guiding implications for the development of multiple disciplines and has emerged as a research hotspot in ocean science in recent years. However, constrained by traditional oceanographic instruments limited to single-point measurements, current observations and analyses of oceanic turbulence still experience considerable shortcomings. To advance oceanic turbulence observations beyond single-point measurements toward comprehensive three-dimensional (3D) field characterization, this study introduces an innovative Holographic Astigmatic Particle Tracking Velocimetry (HAPTV) technology combined with an integrated in situ underwater measurement and processing system. For the first time, this system has successfully acquired 3D two-component (u, v components) ocean flow fields in natural environments. The measured flow velocities reach up to 15 cm/s, with turbulence dissipation rates on the order of 10−4 m2/s3, which is consistent with the hydrodynamic conditions in coastal marine environments. These results demonstrate the feasibility of using HAPTV for field-scale turbulence observations, offering a novel volumetric alternative to conventional single-point techniques. Nevertheless, due to factors such as excessively high concentrations of suspended matter in nearshore waters and z-axis positioning limitations, the accuracy of the flow field results obtained from the initial sea trials still needs to be improved. Full article
(This article belongs to the Special Issue Advances in Ocean Observing Technology and System)
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26 pages, 4151 KB  
Article
Prediction Model for Maritime 5G Signal Strength Based on ConvLSTM-PSO-XGBoost Algorithm
by Jianjun Ding, Kun Yang, Li Qin and Bing Zheng
J. Mar. Sci. Eng. 2026, 14(4), 377; https://doi.org/10.3390/jmse14040377 - 16 Feb 2026
Viewed by 371
Abstract
The accurate prediction of signal strength plays an important role in estimating radio signal quality, thus forming the essential foundation for the planning, optimization, and reliable operation of modern wireless network systems. This paper proposes a new hybrid model for predicting maritime 5G [...] Read more.
The accurate prediction of signal strength plays an important role in estimating radio signal quality, thus forming the essential foundation for the planning, optimization, and reliable operation of modern wireless network systems. This paper proposes a new hybrid model for predicting maritime 5G signal strength, combing Convolutional Long Short-Term Memory (ConvLSTM) with Particle Swarm Optimization-extreme Gradient Boosting (PSO-XGBoost). The model was developed and validated using a dataset comprising 22 columns, 2994 rows, and 21 features, collected via a research vessel in Zhoushan Port, China. Four evaluation metrics, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2) were employed to assess model performance and interpretability. Comparative experiments against various popular models demonstrated the hybrid model’s superior performance in predicting maritime 5G signals. Its accuracy surpassed both standalone ConvLSTM and XGBoost models, while achieving lower MAE and RMSE values compared to various popular models. This study provides a method for predicting coverage conditions based on navigation and environmental data, without relying on radio key performance indicators. Furthermore, it supplies high-quality signal data to advance the modeling of marine communication channels. Full article
(This article belongs to the Special Issue Advances in Ocean Observing Technology and System)
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