Restoring coastal images from high tide to low tide conditions is crucial for applications such as environmental monitoring, coastal planning, and disaster management. Image dewatering, the process of transforming high-tide images into their corresponding low-water-level states to reveal submerged objects, remains a challenging task. Unmanned aerial vehicle (UAV)-based image dewatering presents several unique difficulties. First, there is a scarcity of suitable pre-existing datasets for supervised learning and model training, necessitating extensive data collection to construct an appropriate dataset. Additionally, aligning and matching image pairs is complex. Due to the operational characteristics of UAVs, precisely aligning high-altitude images captured at the same geographic location is difficult, which negatively impacts model training and evaluation. To address these challenges, this study proposes Water2LandNet, an unsupervised UAV image dewatering model based on generative adversarial networks (GANs), which enables training with unpaired images. This approach effectively resolves the data-pairing problem present in our dewatering dataset, OUCD (OUC_UAV_DEWATER). Extensive experiments on the OUCD dataset show that the proposed model effectively removes water from UAV images, as evidenced by qualitative and quantitative evaluations. Furthermore, compared with traditional supervised methods, Water2LandNet demonstrates greater robustness to texture inconsistencies and illumination variations arising from dynamic marine environments. Experimental results confirm that the model can reconstruct visually realistic low-tide scenes even when trained on limited unpaired data, offering a practical solution for large-scale coastal mapping and post-disaster reconstruction.

Localization is a crucial technique for determining target locations in underwater acoustic internet of things (UAIoT) systems. However, the presence of stratified oceanic environments and anchor position uncertainties significantly undermine localization accuracy. To rigorously investigate their adverse effects on localization performance, this study presents a comprehensive theoretical analysis of hybrid RSS/TOA-based localization under stratified propagation and inaccurate anchors in UAIoT systems. First, a stratified acoustic propagation model is established, and the corresponding Cram

r-Rao lower bound (CRLB) is derived for RSS and TOA measurements. The analysis is then extended to a hybrid scenario incorporating anchor inaccuracy using the Banachiewicz-Schur theorem, resulting in a unified expression for the theoretical performance bound. Simulation results confirm the validity of the derived CRLB across diverse scenarios. Compared with using a single ranging modality, the derived lower-bound performance of the proposed hybrid RSS/TOA approach improves by at least 19.8% under accurate-anchor conditions and 24.1% under inaccurate-anchor conditions. The findings provide theoretical benchmarks and practical guidance for designing robust localization strategies in stratified oceanic environments.

Sea surface height (SSH) is a critical parameter for characterizing ocean dynamics and understanding mesoscale eddies, surface currents, and subsurface thermohaline structures. Using along-track data from Haiyang-2B (HY-2B) as a reference, this study evaluated three SSH products for the year 2022 globally and in specific regions: the Copernicus Marine and Environment Monitoring Service (CMEMS) delayed-time (DT) merged gridded product, the CMEMS near-real-time (NRT) merged gridded product, and the Global Ocean Reanalysis and Simulation System (GLORYS) reanalysis product. The CMEMS DT product outperformed the others, achieving the lowest global root mean square error (RMSE, 0.0267 m) and highest correlation, and demonstrated superior signal reconstruction, particularly in dynamically active regions such as the Kuroshio region. The NRT product offers reliable large-scale monitoring capability but shows higher errors in energetic regions, whereas GLORYS, despite its multivariate consistency, exhibits limited skill in resolving smaller-scale signals. For studies focusing on fine-scale and mesoscale features, the DT product is strongly recommended. For real-time large-scale applications, the NRT product is suitable with due regard to limitations in high-dynamic regions. Conversely, when employing GLORYS reanalysis in dynamically active areas, users should carefully evaluate its capacity to reproduce small- and mesoscale processes.

To address the long-standing professional knowledge bottlenecks in scientific marine research and aquaculture, this paper proposes a marine reasoning large language model construction framework based on structured reasoning chain-of-thought (SRCoT) fine-tuning and a knowledge graph (KG). To implement the framework, an indent-driven article heuristic search method is first adopted to construct a marine-domain-specific dataset, followed by the development of a sliding window and weight-matrix-based strategy for dataset deduplication. Subsequently, a marine-domain KG is constructed, and an entity entailment method based on pointwise mutual information vectors is designed. Finally, a model post-training approach integrating SRCoT and three-stage direct preference optimization (DPO) is proposed. The base model is fine-tuned on the marine-domain SRCoT dataset and post-trained using the three-stage DPO strategy. During deployment, the custom-built marine-domain KG is used as an external reference to enhance the model responses. The experimental results demonstrate that the model trained with the proposed framework achieves performance improvements in marine-domain complex reasoning tasks and is effective in mitigating over-reasoning and refining model responses.

Underwater images play an increasingly important role in scientific research and industrial fields such as marine military, marine environmental protection, and marine engineering. However, owing to nonuniform lighting conditions, the quality of underwater imaging is often degraded by remarkable color distortion and detail loss. Although existing traditional underwater image enhancement methods have advanced, they are still limited by scarce and low-quality samples, making it difficult to achieve satisfactory results. In this study, a novel network based on the structure-guided former is proposed to effectively address the challenges of color correction and illumination enhancement in underwater images. The proposed cross-axial compression transformer block preserves the powerful global modeling capacity of transformers while significantly enhancing local feature extraction. In addition, the introduction of structural prior information not only guides the feature reconstruction process in the decoder but also effectively corrects color casts and enhances high-frequency details. Comparative experiments and ablation experiments on publicly available datasets have validated the effectiveness of the proposed method.