Search Results (1,576)

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article

In March 2021, a trial operation set of ocean radar was first introduced in Hong Kong, and then in early 2022 became stably paired up with one operated by the South China Sea Bureau of the Ministry of Natural Resources of China for filling up the meteorological data void over the eastern part of the South China coastal waters. The ocean radar has undergone various enhancements of hardware and software over the years and has reached a stage of providing useful wind observations for weather monitoring purposes in the majority of cases. This paper documents the novel features of the hardware and software of the ocean radar. The performance of the derived wind and other data from the ocean radar is studied by comparing with two sets of weather buoy observations over an extended period of time (one year from June 2024 to July 2025). The quality of the wind data is considered to be reasonable as compared with the international standards of wind measurement errors. The application of the ocean radar wind observations in monitoring different weather systems is also described, including monsoon surges, surface troughs of low pressure, rainstorms and tropical cyclones. The radar is still found to have difficulties in retrieving the winds of high strength (hurricane force winds) and the circulating flow at the same time. Further research work with the ocean radar is also discussed. Full article

In recent years, the discovery of new ultra-deepwater reservoirs has significantly increased both the importance and the complexity of offshore oil production. One of the main challenges in qualifying structures to operate under such severe conditions is the fatigue limit state, particularly fatigue induced by ocean waves. Wave-induced fatigue remains, both at the design stage and during the operation of flexible risers, one of the most demanding issues for engineers responsible for ensuring their structural integrity. This study presents a state-of-the-art review of wave-induced fatigue analysis in flexible risers. It includes a brief historical overview of the problem, a summary of the fatigue assessment methodologies traditionally adopted in offshore engineering, a discussion of pioneering contributions to stress calculation, and an overview of the main research trends currently being pursued. These trends reflect emerging challenges related to fatigue life prediction, including the high computational cost of time-domain analyses, the presence of elevated contaminant levels in transported fluids, the development of new materials to reduce loads or enhance resistance to aggressive environments, and the assessment of remaining service life in the presence of damaged or corroded tensile wires. The potential use of monitored data to reduce uncertainties in numerical modelling is also addressed. Despite the challenges discussed, the main conclusion of this work is that ongoing technological developments are expected to ensure that flexible risers remain key components of offshore oil and gas production systems. Full article

Radiocarbon (¹⁴C) serves as a unique physical tracer for fossil fuel CO₂ (CO_2ff) owing to its absence in ancient fuels. This review synthesizes methodologies and applications of ¹⁴C in quantifying CO_2ff emissions from urban to regional scales. It outlines the theoretical framework for partitioning CO_2ff from other sources using Δ¹⁴C and summarizes advances in sampling strategies and accelerator mass spectrometry (AMS) analysis. Key methodological challenges—including disequilibrium fluxes from terrestrial and oceanic reservoirs, sparse observational networks, and uncertainties in atmospheric inversion models—are critically assessed. The review highlights the pivotal role of ¹⁴C in independently verifying rapid, policy-driven emission reductions during the COVID-19 lockdowns, which provided a clear signal distinct from natural variability. Case studies, with a particular focus on China, demonstrate its utility in tracking spatial gradients and long-term trends. Looking forward, synergistic pathways that integrate multi-tracer observations, expanded monitoring networks, and enhanced modeling are discussed to strengthen the role of ¹⁴C within a comprehensive CO_2ff monitoring and verification framework. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanisms and the nonlinear coupling between delay–Doppler maps (DDMs) and observation geometries. To address these limitations, a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) is proposed accordingly. The model integrates a CNN for local DDM feature extraction, a Transformer encoder for global context modeling, and a cross-attention module to dynamically fuse auxiliary physical parameters. A geophysical model function (GMF)-constrained loss is incorporated to enhance physical consistency. Evaluated on CYGNSS and ERA5 data, the PA-HCTN achieves an RMSE of 1.35 m/s and an R² of 0.75, outperforming existing benchmarks and significantly mitigating high-wind-speed underestimation. In addition, through independent validation using NDBC buoy data from four sites, the results demonstrate the effectiveness of the hybrid architecture and physics-aware design for GNSS-R wind retrieval. Full article

Ocean fronts play a vital role in modulating climate variability, driving material transport, and maintaining the stability of marine ecosystems. Therefore, accurate identification of ocean fronts is of great significance for marine environmental monitoring and resource management. This study focuses on the Northwestern Pacific region and conducts a systematic comparison between two representative deep learning models—U-Net and Mask R-CNN—for automated ocean front detection. The objective is to evaluate the adaptability and strengths of different network architectures in handling multi-scale features, complex background conditions, and boundary delineation, thereby providing a theoretical basis for model selection and application-specific deployment. Experimental results show that U-Net achieves superior spatial consistency in large-scale frontal segmentation, with an IoU of 0.81 and a Dice coefficient of 0.76, while maintaining relatively high computational efficiency. In contrast, Mask R-CNN demonstrates stronger boundary modeling capabilities in detecting small-scale fronts and handling heterogeneous backgrounds, achieving an IoU of 0.78 and a Dice score of 0.73, though at the cost of increased computational demand. Overall, U-Net is more suitable for broad-scale automatic detection of ocean fronts, whereas Mask R-CNN exhibits greater potential in complex scene recognition. Integrating the structural advantages of both models holds promise for further enhancing the stability and accuracy of frontal detection, thereby offering robust technical support for ocean remote sensing analysis and environmental forecasting. Full article

The Sentinel-3 Ocean and Land Colour Instrument (OLCI) is designed for water monitoring. Its 21-spectral bands serve as the basis for the precise retrieval of water quality parameters. However, its coarse resolution restricts the depiction of the spatial distribution of water quality parameters in small inland water bodies. Spatial–spectral fusion is a common method to address the inherent constraints between the spatial and spectral resolutions of sensors. Central to the popular methods is the deep learning-based method. Nonetheless, deep-learning-based models still face challenges in fusing Sentinel-2 Multi-Spectral Instrument (MSI) and Sentinel-3 OLCI data. Here, we propose a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), which effectively integrates OLCI’s spectral advantage and MSI’s spatial resolution. Quantitative evaluation was conducted against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN). The results show that MSA-UGAN achieves the best overall performance: QNR (0.9709) and SSIM (0.9087) are the highest, while SAM (1.1331), spatial distortion (D_S = 0.0389), and spectral distortion (D_λ = 0.0252) are the lowest. This shows that MSA-UGAN can better preserve the spatial details of S2 MSI and the spectral features of S3 OLCI data. Moreover, ERGAS (2.2734) also performs excellently in the comparative experiments. The experiment of Chlorophyll-a inversion using the fused image in Chen Lake revealed a spatial gradient ranging from 3.25 to 19.33 µg/L, with the highest concentrations in the southwestern nearshore waters, likely associated with aquaculture. These results jointly indicate that MSA-UGAN can generate high-spatial-resolution multispectral images, and the fused images can be effectively utilized for water quality monitoring, thereby providing essential data support for the precision management and scientific decision-making regarding inland lakes. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

Oil spills represent a severe environmental hazard, threatening marine and coastal ecosystems, biodiversity, and socio-economic stability. Timely and accurate detection of such incidents is critical for mitigating their ecological and economic consequences. Conventional detection techniques, including manual inspection and satellite-based observation, remain limited by high operational costs, temporal delays, and restricted spatial coverage. To overcome these limitations, this study introduces a comprehensive computer vision framework that addresses two core challenges: (i) the construction of a large-scale, high-quality synthetic oil spill dataset through mask extraction and seamless blending of oil spill regions with diverse oceanic backgrounds, and (ii) the development of a fine-tuned YOLOv11m-seg detection model trained on this enriched dataset. To further enhance the realism and spatial distinctiveness of oil spill textures, the Line Integral Convolution (LIC) is applied to estimate and visualize ocean surface flow patterns, generating coherent streamline textures that simulate the natural diffusion and transport of oil in water. The model exhibited strong generalization and precision, achieving a training accuracy exceeding IoU@0.50-0.95 to 85% over 50 epochs. Evaluation metrics confirmed its reliability, with an F1 score of 94%, precision of 94%, and recall (mAP@0.50) of 94%. These results demonstrate that the developed approach not only enhances dataset diversity but also substantially improves the accuracy and representativeness of real-time oil spill detection in marine environments. Full article

With the growing applications of maritime unmanned systems in environmental monitoring, ocean patrol, and emergency response, achieving efficient multi-platform cooperation in complex and dynamic marine environments remains a critical challenge. Unmanned Aerial Vehicles (UAVs) provide flexible and high-coverage sensing capabilities but are constrained by limited energy capacity, whereas Unmanned Surface Vehicles (USVs) offer long endurance and can serve as mobile platforms and energy supply nodes. Existing studies mostly focus on single-factor optimization, lacking a systematic analysis of the coupled relationships among energy, information (communication and positioning), and task decision making. To address this problem, this paper proposes an Energy–Information–Decision Coupling Optimization Method for Cooperative Maritime Unmanned Systems. A unified coupling model is established to integrate task completion, energy consumption, communication delay, and replenishment scheduling into a multi-objective optimization framework. A bi-level optimization algorithm is designed: the upper layer optimizes USV trajectories and energy supply strategies, while the lower layer optimizes UAV path planning and task allocation. A closed-loop adaptive mechanism is incorporated to achieve optimal cooperation under dynamic tasks and energy constraints. Extensive simulations combined with real-world experimental data are conducted to evaluate the method in terms of mission efficiency, energy balance, communication latency, and system robustness, with ablation studies quantifying the contribution of the coupling module. Results demonstrate that the proposed method significantly outperforms non-coupled or single-factor optimization strategies across multiple performance metrics: it achieves a task completion rate exceeding 93%, reduces total energy consumption by approximately 6% and replenishes waiting latency by over 28% compared with the decoupled baseline method. This effectively enhances the cooperative efficiency and robustness of maritime unmanned systems, and provides theoretical and methodological guidance for large-scale, complex ocean missions. Full article

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

Land subsidence has emerged as a critical geohazard affecting major urban centers worldwide, particularly in coastal and deltaic regions where intensive groundwater extraction and rapid urbanization are prevalent. It is estimated that subsidence threatens more than 1.6 billion people globally, with reported subsidence rates exceeding 100 mm/year in several rapidly urbanizing cities and cumulative ground lowering exceeding 10 m in extreme cases such as Mexico City. This review provides a comprehensive synthesis of the hydrogeological drivers, impacts, and sustainable mitigation pathways of land subsidence based on a systematic literature review of 167 peer-reviewed studies following the PRISMA framework and bibliometric network analysis. The findings confirm that groundwater extraction is the dominant driver, causing pore pressure decline and irreversible consolidation of compressible aquitards, while geological conditions, recharge imbalance, and climate variability strongly influence subsidence magnitude and persistence. The consequences are severe and multidimensional, including increased flood risk, infrastructure damage, groundwater storage loss, ecosystem degradation, and significant socio-economic impacts. Global case studies from major subsiding cities demonstrate that subsidence often contributes more to relative sea-level rise and urban flood vulnerability than climate-driven ocean rise alone. Mitigation strategies, including groundwater regulation, managed aquifer recharge, water-sensitive urban design, geotechnical stabilization, and satellite-based monitoring, have shown effectiveness but remain limited when implemented independently. This study proposes an integrated management framework combining continuous monitoring, hydrogeological assessment, sustainable groundwater management, engineering and nature-based solutions, and governance integration. The findings highlight that early intervention, groundwater sustainability, and coordinated policy actions are essential to reduce subsidence and enhance long-term urban resilience. These insights support the achievement of Sustainable Development Goal 11 (Sustainable Cities and Communities), particularly in strengthening disaster risk reduction and climate resilience in subsidence-prone urban areas. Full article

Sea-surface chlorophyll-a concentration (Chl-a) is a core indicator reflecting phytoplankton biomass and marine ecological conditions. Its spatiotemporal variation patterns are closely related to environmental changes and human activities, especially in coastal waters around heavily populated areas, e.g., the Bohai Sea in China. Benefiting from long time-series ocean-color (i.e., Chl-a provided by Aqua-MODIS) multi-source merged sea surface temperature (SST) and wind speed (i.e., ERA5) and dissolved inorganic nitrogen concentration (DIN) data, this study investigated the long-term variation characteristics of Chl-a in the Bohai Sea and its influencing factors during the period of 2003 to 2022. After rigorous quality control and data reconstruction, this study analyzed the interannual, seasonal, and spatial variation patterns of Chl-a in the Bohai Sea across five ecological functional subregions (Bohai Bay, the Qinhuangdao coast, Liaodong Bay, Laizhou Bay, and the central Bohai Sea), and explored the influence of SST, wind speed, and DIN on variations in Chl-a. The results showed that the spatial distribution of Chl-a in the Bohai Sea exhibited a significant coastal–offshore gradient, with higher concentrations in coastal bays and the Qinhuangdao coast and lower concentrations in the central Bohai Sea. Temporally, despite a long-term trend of first increasing and then decreasing with a peak around 2011, Chl-a underwent a significant regime shift around 2015. After the shift, the average concentration decreased by 0.36 mg/m³ compared with that before the shift. On a seasonal scale, the average Chl-a concentration over the whole Bohai showed the largest decrease in summer (−0.65 mg/m³) and the smallest decrease in winter (−0.21 mg/m³), with contrasting changes among subregions: the Qinhuangdao coast had the most significant decrease (−1.54 mg/m³), while Laizhou Bay remained basically stable. Driver mechanism analysis indicated that Chl-a in the Bohai Sea was significantly negatively correlated with SST (r = −0.51, p = 0.022) and significantly negatively correlated with wind speed (r = −0.77, p < 0.01). Furthermore, both SST and wind speed have undergone significant regime shifts toward a warmer and a windier state, respectively. The timing of these climatic shifts coincided with or preceded the Chl-a regime shift, which may help suppress phytoplankton blooms and maintain lower Chl-a levels. In addition, the surface DIN concentration in Bohai Bay decreased by 23.6% after the Chl-a regime shift, indicating a reduction in nutrient input may be responsible for the decrease in Chl-a in this region. The research results reveal the long-term variation patterns and multi-factor synergistic regulatory mechanism of Chl-a in the Bohai Sea, providing a scientific reference for red-tide monitoring and early warning as well as regional ecological environment management in the Bohai Sea. Full article