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AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing for Geospatial Science".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 6927

Special Issue Editors

Prof. Dr. Gang Yang

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Guest Editor
School of Geography and Remote Sensing, Ningbo University, Ningbo 315211, China
Interests: coastal remote sensing; remote sensing time-series products temporal reconstruction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cunjin Xue

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: marine spatiotemporal data mining theory and methods; monitoring and assessment of ocean sustainable development goals; digital twin ocean
Special Issues, Collections and Topics in MDPI journals
Dr. Yongze Song

E-Mail Website
Guest Editor
School of Design and the Built Environment, Curtin University, Kent Street, Bentley, WA 6102, Australia
Interests: sustainable development; spatial statistics; geospatial methods; urban remote sensing; sustainable infrastructure
Special Issues, Collections and Topics in MDPI journals
Dr. Jianing Zhen

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: remote sensing of coastal wetlands and biodiversity
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoshuang Ma

E-Mail Website
Guest Editor
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Interests: remote sensing image processing and interpretation; remote sensing of environment; synthetic aperture radar; target detection on remote sensing images; image denoising; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the overwhelming support and interest in the previous Special Issue, we are introducing a second edition “AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments” regarding Special Issue “GIS and Remote Sensing in Ocean and Coastal Ecology". We would like to thank all the authors and co-authors who made contributions to the successful first edition of this Special Issue and look forward to more experts' innovative contributions.

GIS and remote sensing are vital technologies for exploring ocean and coastal system dynamics. A variety of satellites and sensors provide spatial–temporal data for the monitoring and assessment of day-to-day changes in the ocean and coastal environments. As integrated parts of the Earth’s ecosystem, ocean and coastal areas are immensely important biologically and socially. These areas are under constant threat due to the anthropogenic activities of unprecedented resource extraction and changing climatic behaviors. The oceans have varied and complex geometry and physiography;  thus, cognizance of their varied characteristics is essential for identifying any implication of these ecosystems. Remote sensing and geographical information system (GIS) techniques have not only proved effective in analyzing the surface characteristics of coastal areas, but also hold much importance in identifying the characteristics of the ocean floor, mapping coastal details, hydrodynamic modeling, and coastal ecological processes and risk assessment.  Recent advances in AI have further revolutionized these technologies, enabling the more efficient and accurate processing of complex geospatial data related to ocean and coastal environments.

We encourage submissions exploring research advancements in and applications of modeling systems and coastal monitoring systems to study the hydrodynamics, morphodynamics, biodiversity, ecological processes, and community succession of the coastal ecosystem; ocean remote sensing, ocean color monitoring, modeling biomass and the carbon of oceanic ecosystems, biogeochemical processes, sea surface temperature (SST) and sea surface salinity, ocean monitoring for oil spills and pollution, coastal erosion, and accretion measurement. Additionally, this Special Issue aims to highlight the integration of AI with remote sensing technologies, including AI-driven remote sensing data processing and intelligent interpretation methods, such as large-model remote sensing indices for the precise identification of key coastal geographical features. We also welcome studies on multimodal sensing data fusion technologies tailored for marine and coastal scenarios, as well as the development of novel lightweight sensors and multi-platform collaboration (e.g., drones and unmanned ships) for marine environmental disaster early warning and monitoring.

We wholeheartedly appreciate your consideration of submitting manuscripts to this Special Issue, entitled “AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments”.   We also kindly request your assistance in sharing this announcement with esteemed colleagues, encouraging them to contribute their expertise to this important field of study.

Together, let us propel advancements in ocean and coastal ecology research forward and contribute to a better understanding of the changes in ocean and coastal environments and their implications for Earth’s ecosystem. 

Dr. Gang Yang
Prof. Dr. Cunjin Xue
Dr. Yongze Song
Dr. Jianing Zhen
Dr. Xiaoshuang Ma
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. Remote Sensing 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 2700 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

  • coastal ocean modeling
  • coastal ocean remote sensing
  • coastal and ocean environment monitoring
  • coastal ocean forecasting
  • biodiversity
  • ecological process and risk assessment
  • artificial intelligence (AI)
  • deep learning
  • UAV
  • radar
  • digital twin
  • ocean and coastal scenario—simulation

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (6 papers)

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Research

20 pages, 7242 KB  
Article
Inversion and Interpretability Analysis of Bottom-Water Dissolved Oxygen in the Bohai Sea Using Multi-Source Remote Sensing Data
by Tao Li, Jie Guo, Shanwei Liu, Yong Jin, Diansheng Ji, Chawei Hou and Haitian Tang
Remote Sens. 2026, 18(5), 838; https://doi.org/10.3390/rs18050838 - 9 Mar 2026
Viewed by 405
Abstract
Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation [...] Read more.
Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R2 = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R2 = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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24 pages, 36830 KB  
Article
GTSegNet: An Island Coastline Segmentation Model Based on Collaborative Perception Strategy
by Yuanyi Zhu, Fangxiong Wang, Yingzi Hou, Zhenqi Cui, Haomiao Yu, Shuai Zhang, Zhiying Liao, Peng Li and Yi Lu
Remote Sens. 2026, 18(4), 607; https://doi.org/10.3390/rs18040607 - 14 Feb 2026
Viewed by 513
Abstract
Island coastline segmentation plays a crucial role in remote sensing image processing, especially when the island environment is complex and the scale is small, making segmentation challenging. The complex morphology of the islands and the small islands are the main causes of boundary [...] Read more.
Island coastline segmentation plays a crucial role in remote sensing image processing, especially when the island environment is complex and the scale is small, making segmentation challenging. The complex morphology of the islands and the small islands are the main causes of boundary blurring and topological discontinuity in the segmentation of the island coast. Therefore, this study proposes GTSegNet, an island coastline segmentation method designed to address the issues of boundary blurring and topological discontinuity in complex backgrounds. First, by introducing the Graph Contextual Modeling Module (GCB), the model captures global information and addresses the issue of neglected local features due to complex backgrounds and scale differences, thereby improving the model’s ability to discern blurry boundaries. Secondly, the Morphological Topology-Aware Refinement Module (TARM) is used for boundary sharpening and false response suppression, specifically addressing the issue of topological discontinuity, thus improving the accuracy of boundary localization and the continuity of topological structures. The two modules work synergistically, significantly improving the accuracy of the boundaries and topological continuity of the island coastline. Training and comparative experiments conducted on the newly constructed island coastline dataset demonstrate that GTSegNet achieves an outstanding performance with an mIoU of 96.96% and a Recall of 98.54%. Compared to other remote sensing semantic segmentation methods, GTSegNet consistently exhibits stable advantages in both quantitative and qualitative assessments, showcasing its great potential for large-scale marine mapping and macro-scale monitoring tasks. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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23 pages, 6999 KB  
Article
An Integrated Feature Framework for Wetland Mapping Using Multi-Source Imagery
by Liansong Zhang, Zixuan Wang, Jifei Wang, Qiang Hu, Yonglei Chang, Zhong Lu and Jinqi Zhao
Remote Sens. 2025, 17(22), 3737; https://doi.org/10.3390/rs17223737 - 17 Nov 2025
Cited by 1 | Viewed by 666
Abstract
Accurate extraction of land cover information and effective classification strategies are crucial for reliable wetland mapping. Data-driven approaches, such as convolutional neural networks (CNNs), demonstrate strong capability in modeling complex nonlinear relationships and learning hierarchical feature representations. However, these methods typically require large [...] Read more.
Accurate extraction of land cover information and effective classification strategies are crucial for reliable wetland mapping. Data-driven approaches, such as convolutional neural networks (CNNs), demonstrate strong capability in modeling complex nonlinear relationships and learning hierarchical feature representations. However, these methods typically require large labeled datasets, are prone to overfitting and often lack interpretability. In contrast, knowledge-driven approaches based on physical models and expert-defined indices are characterized by interpretable and stable features, but their dependence on predefined formulations restricts flexibility and limits adaptability in heterogeneous environments. To address these limitations, this paper proposes an integrated framework that combines knowledge-driven and data-driven features from multi-source imagery to form a complementary feature set for wetland mapping. All extracted features are incorporated into a Random Forest (RF) classifier, enabling effective utilization of the high-dimensional and heterogeneous feature set. In addition, knowledge-driven and data-driven features are visualized and their importance is analyzed to verify their roles in classification and improve model interpretability. The Yellow River Delta and the Qilihai Wetland, representing study areas with different scales and data conditions, are selected to assess the robustness of the proposed method. The experimental results demonstrate that the proposed approach achieved the best classification performance among all comparative experiments. In the Yellow River Delta and Qilihai Wetland study areas, the Overall Accuracy (OA), Kappa coefficient, and F1-score reached 90.91%, 0.8898, 0.9136 and 91.31%, 0.8893, 0.9308, respectively. In addition, the integration of knowledge-driven and data-driven features effectively proves effective in enhancing classification robustness and improves the interpretability of feature representations. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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21 pages, 2271 KB  
Article
A Domain Adaptation-Based Ocean Mesoscale Eddy Detection Method Under Harsh Sea States
by Chen Zhang, Yujia Zhang, Shaotian Li, Xin Li and Shiqiu Peng
Remote Sens. 2025, 17(19), 3317; https://doi.org/10.3390/rs17193317 - 27 Sep 2025
Viewed by 849
Abstract
Under harsh sea states, the dynamic characteristics of ocean mesoscale eddies (OMEs) become significantly more complex, posing substantial challenges to their accurate detection and identification. In this study, we propose an artificial intelligence detection method for OMEs based on the domain adaptation technique [...] Read more.
Under harsh sea states, the dynamic characteristics of ocean mesoscale eddies (OMEs) become significantly more complex, posing substantial challenges to their accurate detection and identification. In this study, we propose an artificial intelligence detection method for OMEs based on the domain adaptation technique to accurately perform pixel-level segmentation and ensure its effectiveness under harsh sea states. The proposed model (LCNN) utilizes large kernel convolution to increase the model’s receptive field and deeply extract eddy features. To deal with the pronounced cross-domain distribution shifts induced by harsh sea states, an adversarial learning framework (ADF) is introduced into LCNN to enforce feature alignment between the source (normal sea states) and target (harsh sea states) domains, which can also significantly improve the segmentation performance in our constructed dataset. The proposed model achieves an accuracy, precision, and Mean Intersection over Union of 1.5%, 6.0%, and 7.2%, respectively, outperforming the existing state-of-the-art technologies. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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19 pages, 11572 KB  
Article
Reconstruction of the Subsurface Temperature and Salinity in the South China Sea Using Deep-Learning Techniques with a Physical Guidance
by Qianlong Zhao, Shaotian Li, Yuting Cai, Guoqiang Zhong and Shiqiu Peng
Remote Sens. 2025, 17(17), 2954; https://doi.org/10.3390/rs17172954 - 26 Aug 2025
Cited by 1 | Viewed by 1841
Abstract
In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the [...] Read more.
In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the deep learning algorithms of residual and channel attention with the physical constraints of vertical modes of T/S profiles decomposed by empirical orthogonal functions (EOFs). The results from a series of single point experiments show that FFPG-net outperforms the CNN or CNN-PG (without physical guidance or feature fusion) in the reconstruction of subsurface T/S in a region of the South China Sea (SCS), with monthly mean RMSEs of 0.31 °C (0.35 °C) and 0.06 psu (0.07 psu) for the reconstructed T/S profiles in winter (summer), averaged over the water depth of 1200 m and the study area. In addition, the performance of the FFPG-net can be improved significantly by incorporating full surface currents or geostrophic currents derived from SSH into the input variables for training the neural network. The preliminary application of FFPG-net in the SCS using satellite-derived sea surface observations indicates that FFPG-net is reliable and feasible for reconstructing subsurface ocean thermal fields in real situations. Our study highlights the advantages and necessity of combining deep learning algorithms with physical constraints in reconstructing subsurface T/S profiles. It provides an effective tool for reconstructing the subsurface global ocean from remote-sensing sea surface observations in the future. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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29 pages, 6375 KB  
Article
“Ground–Aerial–Satellite” Atmospheric Correction Method Based on UAV Hyperspectral Data for Coastal Waters
by Xinyuan Su, Jianyong Cui, Jinying Zhang, Jie Guo, Mingming Xu and Wenwen Gao
Remote Sens. 2025, 17(16), 2768; https://doi.org/10.3390/rs17162768 - 9 Aug 2025
Cited by 3 | Viewed by 1975
Abstract
In ocean color remote sensing, most of the radiative energy received by sensors comes from the atmosphere, requiring highly accurate atmospheric correction. Although atmospheric correction models based on ground measurements—especially the Ground-Aerial-Satellite Atmospheric Correction (GASAC) method that integrates multi-scale synchronous data—are theoretically optimal, [...] Read more.
In ocean color remote sensing, most of the radiative energy received by sensors comes from the atmosphere, requiring highly accurate atmospheric correction. Although atmospheric correction models based on ground measurements—especially the Ground-Aerial-Satellite Atmospheric Correction (GASAC) method that integrates multi-scale synchronous data—are theoretically optimal, their application in nearshore areas is limited by the lack of synchronous samples, pixel mismatches, and nonlinear atmospheric effects. This study focuses on Tangdao Bay in Qingdao, Shandong Province, China, and proposes an innovative GASAC method for nearshore waters using synchronized surface spectrometer data and UAV hyperspectral imagery collected during Sentinel-2 satellite overpasses. The method first resolves pixel mismatch issues in UAV data through Pixel-by-Pixel Matching (MPP) and applies the Empirical Line Model (ELM) for high-accuracy ground-aerial atmospheric correction. Then, based on spectrally unified UAV and satellite data, a large amount of high-quality spatial atmospheric reference data is obtained. Finally, a Transformer model optimized by an Exponential-Trigonometric Optimization (ETO) algorithm is used to fit nonlinear atmospheric effects and perform aerial-to-satellite correction, forming a stepwise GASAC framework. The results show that GASAC achieves high accuracy and good generalization in local areas, with predicted remote sensing reflectance reaching R2 = 0.962 and RMSE = 12.54 × 10−4 sr−1, improving by 5.2% and 23.5%, respectively, over the latest deep learning baseline. In addition, the corrected data achieved R2 = 0.866 in a Chl-a retrieval model based on in situ measurements, demonstrating strong application potential. This study offers a precise and generalizable atmospheric correction method for satellite imagery in nearshore water quality monitoring, with important value for coastal aquatic ecological sensing. Full article
(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)
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