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Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments
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Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments

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 August 2026 | Viewed by 3772

Special Issue Editors

Prof. Dr. Yuanzhi Zhang

E-Mail Website
Guest Editor
School of Marine Sciences, Nanjing University of Information Science and Technology, 219 Road Ninglu, Pukou District, Nanjing 210044, China
Interests: microwave remote sensing of land surface; environmental remote sensing; geo-data analysis; spatial statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Li

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN 46202, USA
Interests: optical remote sensing; inland and coastal water quality; harmful algal bloom; machine learning and big data processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongmei Chen

E-Mail Website
Guest Editor
Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: remote sensing; GIS; change detection; feature extraction; environmental modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Po Hu

E-Mail Website
Guest Editor
Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Interests: Data modelling; upwelling, storms and waves; oceanic dynamics; climate change; typhoon and its impact; AI application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of remote sensing big data, combined with advances in artificial intelligence (AI), is transforming our ability to assess and understand estuarine, coastal, and marine environments at global, regional, and local scales. These environmental settings have become focal points of public concern due to their sensitivity to both natural and human-induced changes.

Rapid urbanization, coastline change, and sea level rise have amplified the impacts of climate change. These processes significantly affect or contribute to land–ocean–atmosphere interactions, often with destructive consequences for human development and coastal resilience.

With the emergence of cloud computing, machine learning, and other AI technologies, remote sensing big data can now be processed and analyzed at unprecedented speed and scale. This leads to new opportunities for advancing scientific understanding and developing solutions for the sustainable management of coastal and marine systems.

This Special Issue will highlight cutting-edge research and innovative applications of big data and AI in estuarine, coastal, and marine sciences. We invite original research articles and review papers addressing, but not limited to, the topics outlined below:

  • Application of AI and big data methods in oceanography;
  • Big data processing for coastal and ocean environments;
  • Coastal–ocean environments and ecosystems with AI;
  • Water pollution and red tide (or oil spills) with AI or big data;
  • Coastal erosion and coastline change using big data or AI
  • Sea level rise and climate change with AI;
  • Ocean–atmosphere interaction with big data;
  • Ocean reanalysis big data;
  • Wind field and wave estimation using AI;
  • Typhoon impact and disaster using big data and AI;
  • Estuarine engineering and coastal infrastructure with AI or big data.

Prof. Dr. Yuanzhi Zhang
Prof. Dr. Lin Li
Prof. Dr. Dongmei Chen
Dr. Po Hu
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 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

  • marine science
  • remote sensing big data
  • AI
  • ocean–atmosphere interaction
  • coastal resilience and disaster
  • engineering and infrastructure

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Published Papers (5 papers)

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Research

29 pages, 10384 KB  
Article
OShipNet: Occlusion Ship Detection Based on Multidomain Fusion and Multiscale Refinement
by Shengying Yang, Haowei Luo, Zhenyu Xu, Jing Yang and Wei Zhang
J. Mar. Sci. Eng. 2026, 14(9), 804; https://doi.org/10.3390/jmse14090804 - 28 Apr 2026
Viewed by 271
Abstract
The growth in international trade has precipitated operational demands on port facilities, mandating the development of advanced intelligent monitoring systems. Existing ship detection algorithms struggle with feature confusion and difficulty in extracting contextual features under occlusion, which reduces the discriminability between object features [...] Read more.
The growth in international trade has precipitated operational demands on port facilities, mandating the development of advanced intelligent monitoring systems. Existing ship detection algorithms struggle with feature confusion and difficulty in extracting contextual features under occlusion, which reduces the discriminability between object features and background noise. This leads to positional misalignment and mismatching of similar targets, which reduce the detection accuracy. To resolve this, we propose OShipNet, an architecture engineered to optimize feature fusion and refinement for occluded ship detection. First, we design the OShipNeXt backbone network, which provides complementary feature representation in frequency and spatial domains. This approach enables the reconstruction of global–local semantic associations for occluded objects, enhancing feature representation and improving detection accuracy. Secondly, to further refine target boundaries, we develop a Multiscale Pooling Attention Module (MSPAM) to enhance contextual awareness and better capture occluded edge features. Furthermore, we propose a dual-path cooperative loss function that mitigates the effects of low-quality bounding boxes. Comprehensive evaluations on the MVDD13 dataset demonstrate the robustness of OShipNet, which achieved 94.98% mAP@50 and 84.37% mAP@50-95, demonstrating advantages over existing object detection methods and establishing an effective framework for intelligent port monitoring. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments)
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23 pages, 10034 KB  
Article
A Remote Sensing Monitoring System for Marine Red Tides Based on Targeted Negative Sample Selection Strategies
by Qichen Fan, Yong Liu, Yueming Liu, Xiaomei Yang and Zhihua Wang
J. Mar. Sci. Eng. 2026, 14(6), 556; https://doi.org/10.3390/jmse14060556 - 17 Mar 2026
Viewed by 458
Abstract
The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal [...] Read more.
The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments)
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18 pages, 3856 KB  
Article
Remote Sensing Retrieval of Chlorophyll-a in Turbid Waters Using Sentinel-3 OLCI: Application of Machine Learning in the Pearl River Estuary (China)
by Yuanzhi Zhang, Fang Wu, Ka Po Wong, Jiajun Feng, Jinyi Chang and Jianlin Qiu
J. Mar. Sci. Eng. 2026, 14(4), 360; https://doi.org/10.3390/jmse14040360 - 13 Feb 2026
Cited by 1 | Viewed by 850
Abstract
The accurate remote sensing retrieval of chlorophyll-a (Chla) concentrations in highly turbid estuarine waters remains challenging due to complex optical conditions. In this study, a small sample machine learning-based retrieval framework tailored for limited training samples was developed for the Pearl River Estuary [...] Read more.
The accurate remote sensing retrieval of chlorophyll-a (Chla) concentrations in highly turbid estuarine waters remains challenging due to complex optical conditions. In this study, a small sample machine learning-based retrieval framework tailored for limited training samples was developed for the Pearl River Estuary (PRE) by integrating Sentinel-3 OLCI satellite imagery with long-term fixed-station Chla observations from the Hong Kong Environmental Protection Department. Normalized remote sensing reflectance features derived from multiple OLCI spectral bands were used as model inputs, and the performance of support vector regression (SVR) and a back propagation neural network (BPNN) was evaluated and compared with those of traditional second-order polynomial models. The results show that SVR achieves the best overall performance on both training and independent testing datasets, with a higher accuracy, smaller systematic bias, and more stable generalization capability, demonstrating its effectiveness in capturing complex nonlinear relationships under limited sample conditions. Specifically, for the training and testing datasets, the correlation coefficients between SVR-predicted and measured Chla reach 0.88 and 0.78, RMSEs are 1.75 and 1.23 mg/m3, and biases are −0.29 and 0 mg/m3, respectively. The retrieval results further reveal the clear spatiotemporal patterns of Chla concentration in the PRE, characterized by a west–high and east–low spatial distribution and pronounced seasonal migration. Elevated Chla concentrations occur mainly in the lower estuary during summer, retreat toward the upper estuary in winter, and shift to the middle estuary during spring and autumn. This study provides a practical methodological reference for the operational remote sensing monitoring of water quality in optically complex and highly turbid estuarine environments. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments)
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22 pages, 3427 KB  
Article
FCS-Net: A Frequency-Spatial Coordinate and Strip-Augmented Network for SAR Oil Spill Segmentation
by Shentao Wang, Byung-Won Min, Depeng Gao and Yue Hong
J. Mar. Sci. Eng. 2026, 14(2), 168; https://doi.org/10.3390/jmse14020168 - 13 Jan 2026
Viewed by 548
Abstract
Accurate segmentation of marine oil spills in synthetic aperture radar (SAR) images is crucial for emergency response and environmental remediation. However, current deep learning methods are still limited by two long-standing bottlenecks: first, multiplicative speckle noise and complex background clutter make it difficult [...] Read more.
Accurate segmentation of marine oil spills in synthetic aperture radar (SAR) images is crucial for emergency response and environmental remediation. However, current deep learning methods are still limited by two long-standing bottlenecks: first, multiplicative speckle noise and complex background clutter make it difficult to accurately delineate actual oil spills; and second, limited receptive fields often lead to the geometric fragmentation of elongated, irregular oil films. To surmount these challenges, this paper proposes a novel framework termed the Frequency-Spatial Coordinate and Strip-Augmented Network (FCS-Net). First, we leverage the ConvNeXt-Small backbone to extract robust hierarchical features, utilizing its large kernel design to capture broad contextual information. Second, a Frequency-Spatial Coordinate Attention (FS-CA) module is proposed to integrate spatial coordinate encoding with global frequency-domain information. Third, to maintain the morphological integrity of elongated targets, we introduce a Strip-Augmented Pyramid Pooling (SAPP) module which employs anisotropic strip pooling to model long-range dependencies. Extensive experiments on the multi-source SOS dataset demonstrate the effectiveness of FCS-Net. The proposed method achieves state-of-the-art performance, reaching an mIoU of 87.78% in the Gulf of Mexico and 89.62% in the challenging Persian Gulf, outperforming strong baselines and demonstrating superior robustness in complex ocean scenarios. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments)
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17 pages, 5398 KB  
Article
Life-Cycle Impacts of Artificial Islands on Shoreline Evolution: A High-Frequency Satellite-Based Assessment
by Xiaodong Zhang, Zenglei Yue, Gang Liu and Yanhui Wang
J. Mar. Sci. Eng. 2025, 13(11), 2211; https://doi.org/10.3390/jmse13112211 - 20 Nov 2025
Viewed by 1053
Abstract
Offshore artificial islands are increasingly constructed along sedimentary coasts, yet their life-cycle impacts on adjacent beaches remain poorly quantified. Here we analyze 21 years of high-frequency satellite observations to assess how the building and removal of two adjacent islands (Ridao and Yuedao) altered [...] Read more.
Offshore artificial islands are increasingly constructed along sedimentary coasts, yet their life-cycle impacts on adjacent beaches remain poorly quantified. Here we analyze 21 years of high-frequency satellite observations to assess how the building and removal of two adjacent islands (Ridao and Yuedao) altered shoreline evolution at Riyue Beach, China. A total of 884 Landsat and Sentinel-2 images were processed with sub-pixel shoreline detection, georeferenced against a stable coastal highway and corrected for tidal elevation to derive mean water shoreline positions along 19 transects. Results show that island emplacement triggered rapid salient growth (62–86 m yr−1) opposite the structures and temporary erosion on their flanks. A full tombolo formed on the lee side of Ridao within four years. As the salient widened, the former eroding flanks switched from an “erosional shadow” to a “secondary shelter” and began to re-accrete. The study also reveals lateral coupling between the islands; combined with previous work, it encompasses a critical D/L (offshore distance/alongshore length) threshold of 0.44–0.9 for salient–tombolo formation. Rather than perpetual dredging, we recommend accepting the impending landward connection of Ridao Island. This strategy would eliminate maintenance costs and provide a practical reference for the sustainable management of artificial island shorelines. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence Applications to Estuarine-Coastal-Marine Environments)
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