Atmospheric and Oceanic Sciences

Spaceborne Synthetic Aperture Radar (SAR) provides all-weather, day and night and wide-area imaging capability, and plays a critical role in maritime surveillance. While substantial progress has been achieved in SAR ship detection, SAR ship classification remains relatively underexplored, mainly due to the scarcity of reliable category labels. Automatic Identification System (AIS) provides vessel identity, type, and dynamic trajectory information, and thus offers vessel type information that is difficult to obtain directly from SAR imagery. This paper proposes a fine-grained nine-category SAR ship classification method based on AIS association, which reorganizes the original AIS vessel types into nine fine-grained categories of SAR ship, transfers AIS vessel type information to SAR detection through a global optimal matching strategy, and supports SAR-only vessel category recognition. By retaining only high-confidence SAR and AIS matched pairs and cropping the corresponding SAR ship chips, an SAR ship classification dataset containing 4472 ship chips across the nine categories is constructed. In Monte Carlo experiments based on real AIS records, the proposed association strategy achieves more reliable high-confidence label generation than the compared association methods under close ship ambiguity, spatial perturbation, distractor AIS candidates, and AIS static size errors. In the benchmark experiment on the constructed classification dataset, ConvNeXt-Tiny achieves the best performance among the compared mainstream classifiers. These results demonstrate that AIS association can provide reliable category supervision for SAR ship classification, and the trained classifier can perform ship classification using SAR imagery alone.

Imagery is fundamental to modern scientific research, making robust intrinsic camera calibration indispensable for accurate visual inference. The checkerboard-based calibration method has long been favored for its simplicity and ease of deployment and is widely used even in mission-critical computer vision pipelines. However, its limitations in modeling high-precision camera geometry can compromise downstream performance in tasks requiring geometric accuracy. In this work, camera calibration is revisited through the lens of photogrammetric self-calibration (PSC), and it is demonstrated that the PSC consistently outperforms the checkerboard method in both accuracy and precision across a range of vision tasks, including 3D reconstruction with structure from motion (SfM), visual simultaneous localization and mapping (SLAM), and novel-view synthesis and reconstruction. Our findings advocate for a paradigm shift toward calibration methods that better reflect the physical and projective properties of camera systems in real-world deployments for critical computer vision applications.

Under degraded operations in urban rail transit systems, the coordinated state between passengers and trains undergoes significant changes due to factors such as train frequency reduction. This creates a critical mismatch between passenger demand and transport capacity, leading to rapid passenger accumulation at stations and potential safety hazards from abnormal crowding and systemic congestion. This study develops an integrated simulation model that combines train schedules with passenger arrival patterns to quantify how altered departure sequences generate supply-demand imbalances. A bounded rationality route choice model incorporating four decision-making dimensions - time value, transfer burden, service degradation impact, and route familiarity - captures passenger behavioral adaptations during degraded operations. The experimental results indicate that under abnormal train operation scenarios, the model incorporating bounded rationality assumptions is closer to actual observed values compared with the fully rational benchmark model. In the L2 degradation scenario, the deviation between the bounded rationality model and the actual values is controlled within 0.6%-1.3%, whereas the deviation of the fully rational model is approximately 12%. Concurrently, passenger flow propagation intensity in core sections rises from 0.3 to 0.7, showing a clear evolution from localized accumulation to regional diffusion. The research provides an effective methodology for identifying passenger flow dynamics under degraded operational scenarios and supports decision-making for service optimization and passenger flow management.

Marine spatial planning (MSP) is gaining prominence as an ocean governance tool in the Pacific Islands, yet its application and transformative potential remain under-examined. Drawing on participant observations and interviews with Pacific actors, this study critically assesses whether MSP is fit for purpose in advancing transformative ocean governance in the Pacific Islands. An integrative framework linking collaborative, polycentric, and adaptive governance to six IPBES transformative change clusters is used to evaluate the results. Findings indicate that MSP in the Pacific Islands currently operates in a reformist mode, contributing to improved coordination, dialogue, and awareness of ocean-use conflicts rather than deep governance transformation. Collaborative governance is evident in the rise of interministerial ocean committees and co-creation processes, yet meaningful engagement of Indigenous Peoples and Local Communities in decision-making remains weak. Polycentric governance is partially visible through multi-actor steering committees but often manifests as fragmented and weakly coordinated, with limited decision-making authority. Adaptive governance is the least developed dimension, constrained by gaps in data, technical expertise, institutional continuity, and learning mechanisms. Yet, emerging MSP practices, including knowledge co-creation and revitalization of community rights, indicate nascent shifts toward transformative ocean governance. The study concludes that MSP in the Pacific Islands contexts examined in this study holds transformative potential but will require deeper institutional restructuring, authority redistribution, sustained knowledge co-production, and stronger adaptive learning systems to disrupt business-as-usual governance.

This special issue addresses emerging technologies and future directions in air quality research by integrating spaceborne observations with in situ measurements, data fusion frameworks, and advanced computational techniques. The collective findings of the contributing studies offer a valuable resource for researchers, practitioners, and policymakers seeking to understand and quantify air pollution across diverse environments. The methodologies presented across these papers establish a foundation for identifying pollution sources and characterizing pollutant transport and transformation processes at the regional scale, supporting the stabilization of air quality management systems. To reduce the health burden of ambient air pollution, the contributing authors collectively underscore the need to raise awareness around reducing anthropogenic emissions and to advance space-driven data fusion systems, including expanding monitoring infrastructure, operationalizing AI/ML-driven analytical pipelines, implementing science-informed emission control policies, and fostering meaningful community engagement.

Metal sulfides are promising for capturing gaseous Hg 0, but their adsorption capacities are limited due to Hg 0 adsorption only on superficial unsaturated sulfur (S) coordination. Therefore, enabling saturated S coordination beneath the surface to participate in Hg 0 adsorption is an innovative and effective strategy. In this study, surface adsorption of Hg 0 on CuS was converted into a replacement reaction with a stoichiometric ratio of 1:6 through intermittent acid washing, allowing 2/3 of sulfur in CuS to contribute to Hg 0 adsorption, significantly increasing the capacity theoretically to 348 mg g –1 . The formation of a Cu vacancy due to the dissociation of four-coordinated Cu1 with one dangling bond and the transformation of four-coordinated Cu0 without a dangling bond to Cu1 alternately happened during each acid washing and Hg 0 adsorption, respectively, resulting in a chain reaction of the substitution of Cu in CuS by Hg 0 . Ultimately, 1/4 of four-coordinated Cu 1.5+ in CuS can be isomerous-substituted by Hg and dissociated into the acid washing solution, resulting in the growth of one-dimensional HgS in destroyed CuS crystals. CuS with intermittent acid washing demonstrates significant potential for recovering gaseous Hg 0 from flue gas, with Hg content in spent CuS exceeding 20%, offering a valuable approach for resource recycling.

A cross-disciplinary literature on just transitions shows that shifting to a lower-carbon economy can take jobs, tax revenues, and people from fossil-fuel-dependent communities. But few studies examine how pollution left by extractive industries may amplify and perpetuate losses. We conducted interviews with 39 stakeholders involved in remediating waterways polluted by abandoned coal mines in Pennsylvania, which once led coal production in the U.S. We find that, in our cases, water pollution acts as a “signal of neglect” that suppresses recreational use and economic activity and fosters pessimism among local residents. In our sample, communities with more social capital, defined by strong leadership and well-organized fishing and environmental networks, have successfully remediated numerous waterways. Although there was not deliberate coordination between remediation and economic development initiatives, participants report that cleaned waterways support emerging tourism and outdoor recreation industries. Our findings suggest that in some contexts, reversing signals of neglect can foster community optimism and local economic resilience. However, current policies to support waterway remediation depend on existing social capital and may therefore bypass socially-frayed communities. Our findings motivate policies to build grassroots capacity and to streamline remediation processes to reduce burdens on that capacity.

Forage fish are small, schooling fish that play a vital role in marine ecosystems, serving as a primary food source for seabirds, marine mammals, and commercially important species such as salmon. Despite their ecological importance, forage fish distributions remain understudied in the Canadian Salish Sea. Improved knowledge of their habitat uses and population dynamics is essential for effective conservation and management. In this study, a predictive geospatial model was developed to identify and map forage fish species “hot-spots” in the Canadian Salish sea. The model was constructed using 2,629 observations of six common forage fish species taken from Fisheries and Oceans Canada’s juvenile Pacific salmon survey, spanning 2000-2023. Discussions with a panel of local experts were then conducted to identify 18 environmental variables that were likely to influence the distributions of the forage species. Forage fish occurrence probabilities were estimated using a stacked ensemble approach which combined Neural Network, Generalized Linear Mixed Effects, and XGBoost models. The stacked ensemble achieved an average AUC of 0.73 across a 3-fold cross-validation, indicating strong overall ability to distinguish between presence and absence. Predicted presences were accurate 98% of the time (precision), and the model successfully detected 72% of all true presences (recall). Wind-driven surface current speeds, dissolved organic nitrogen (DON), and mesozooplankton biomass were found to be the most important predictor variables. A predictive map of forage fish hotspots was generated and reviewed, providing insight into the spatial distribution of the six species. Hotspots were identified around the Fraser River delta, along the southern coast of Vancouver Island, and throughout the Gulf Islands. Generally, higher probabilities were associated with inlets, such as Desolation Sound. The lowest probabilities of occurrence were associated with deep waters in the central Strait of Georgia.

To address the challenges of severe optical attenuation and dynamic feature extraction for moving target depth retrieval in complex underwater remote sensing environments, this paper proposes a dynamic target depth estimation method based on multi-source data fusion. Taking optical RGB imagery and neuromorphic vision (NeuroIV) data as joint inputs, the proposed method constructs a three-channel feature extraction and fusion network. By leveraging a hypergraph structure, it establishes association weights between dynamic (temporal) and static (spatial) nodes to capture spatiotemporal correlations. To efficiently process the high-dimensional multi-modal data, the traditional dot-product attention is replaced with element-wise multiplication, significantly reducing computational complexity. Furthermore, a lightweight deformable attention pyramid (DAP) and diffusion model is introduced to refine depth image edges, effectively suppressing discontinuities and abruptness in the estimation results. Compared to single-modality optical imagery, the fused multi-modal data yields a superior signal-to-noise ratio and foreground contrast, achieving an improvement of over 20% in the MAE index. These results validate the effectiveness and superiority of the proposed multi-modal fusion strategy for dynamic target observation and depth retrieval in aquatic environments.

In many basins worldwide, high-quality freshwater is becoming increasingly scarce, while energy production increasingly requires freshwater [...]

Remote sensing ship detection plays a pivotal role in maritime surveillance, safety assurance, and traffic management. However, current detection methods often face significant challenges due to complex sea-surface background noise, large target-scale variations, and edge-hardware limitations. In this paper, we propose RepLite-YOLO, a lightweight detection framework based on YOLOv11n. Specifically, to alleviate irreversible spatial information loss during downsampling, we adopt the ADown module, originally introduced in YOLOv9, to generate spatially complementary features through its two-branch downsampling mechanism. This design helps preserve salient hull-edge responses while suppressing part of the random sea-surface interference, thereby improving feature robustness for small ship targets. To achieve substantial structural streamlining while maintaining competitive representational capacity under strict hardware constraints, we design the C3k2_OREPA_RS module, utilizing online re-parameterization (OREPA) to efficiently reconstruct deep layers without additional re-parameterization-induced inference operations. Furthermore, we construct the ELANFusion_Block by integrating Depthwise Separable Convolutions (DSC) into the ELAN paradigm to alleviate the multi-scale aggregation bottleneck, and tailor the Detect_DWLite head for highly compressed decoupled prediction. Experimental results show that RepLite-YOLO achieves a favorable balance between detection accuracy and computational efficiency. Compared with YOLOv11n, it reduces the number of parameters by 57.4% and GFLOPs by 49.2%, while maintaining competitive detection accuracy with slight mAP@50 improvements of 1.2 and 1.3 percentage points on the Vessel dataset and Ship Detection dataset, respectively.

Object detection in UAV aerial imagery is challenged by dense small targets, large-scale variation, complex backgrounds, and strict onboard computation limits. To address these issues, this paper proposes SMMNet (Structured-diffusion Mamba Mixture Network), a lightweight plug-and-play detection framework evaluated with YOLO family detectors. SMMNet contains three modules. The Structured Diffusion Feature Extractor (SDFE) uses anisotropic diffusion to preserve boundary-sensitive features during downsampling. The Mamba-driven Receptive-field Context Aggregator (MRCA) performs multi-directional selective state-space scanning to capture long-range context with linear complexity. The Mask-guided Bayesian Box Refinement (MBBR) applies a MAP-inspired confidence-adaptive box update using MobileSAM mask evidence and ELBO-based false-positive filtering. Using YOLOv13-S as the main detector, SMMNet achieves 32.8% mAP@0.5:0.95 and 52.6% mAP@0.5 on VisDrone2019 at 87 FPS on an NVIDIA A800 GPU, improving the YOLOv13-S baseline by 3.6 and 4.5 points, respectively. The added modules reduce throughput compared with the detector-only baseline (168 FPS), but the resulting 87 FPS remains real-time and provides a favorable accuracy–latency trade-off. Three independent-seed runs further show a mean paired gain of 3.60 ± 0.10 mAP on VisDrone2019, 2.53 ± 0.12 mAP on DroneVehicle, and 2.77 ± 0.06 mAP on SeaDronesSee for the YOLOv13-S setting. Additional experiments on DroneVehicle and SeaDronesSee, together with cross-backbone evaluations on YOLOv5/v6/v7/v8/v10/v11/v13 across different UAV benchmarks, show aligned performance trends under matched settings. Edge deployment on an NVIDIA Jetson Orin NX reaches 30 FPS under TensorRT FP16 inference at 15 W TDP, indicating the suitability of SMMNet for resource-constrained UAV perception.

Abstract Overharvesting of wild medicinal plants threatens biodiversity, rural livelihoods, and the long-term sustainability of harvested populations worldwide. Wild American ginseng (Panax quinquefolius L.) is listed on Appendix II of the Convention on International Trade in Endangered Species (CITES) and, as a result, is regulated in the United States under a federally coordinated, age-based harvest framework intended to sustain natural populations. However, age-based criteria assume strong, consistent relationships between plant age, size, and reproductive capacity—relationships that do not hold up for many other plant and animal species, and have been questioned for American ginseng. We test whether size-based metrics provide a stronger and more practical basis for regulating ginseng harvest than chronological age. We measured age, aboveground traits, root biomass, and reproductive output for wild-simulated ginseng plants (5–10 years old) across four sites spanning substantial environmental heterogeneity. Leaf area, estimated from a simple field measurement, explained 75% of the variation in root dry mass, far exceeding the explanatory power of age (12%) or the combination of age and leaf count (36%), representing the current regulatory approach. Leaf area also strongly predicted the probability of reproduction and maximum seed production, with larger plants consistently outperforming smaller individuals, regardless of age. Our results demonstrate that age-based harvest criteria are weak proxies for the biological attributes most relevant to conservation and sustainability. A simple, field-measurable size metric better reflects ginseng performance and could underpin a transition to size-based regulation, analogous to widely used approaches in fisheries management.

In complex marine environments, path-following control of unmanned surface vessels (USVs) faces numerous challenges, including environmental disturbances, dynamic nonlinearities, and underactuated systems. To overcome the limitations of traditional line-of-sight/PID control in terms of robustness and adaptability, this study proposes a hybrid control architecture combining improved deep deterministic policy gradient (IDDPG) and model predictive control (MPC). The IDDPG algorithm, as the upper-level decision-making module, utilizes deep reinforcement learning to generate optimal heading angle increment commands by learning the environmental state. The MPC, as the lower-level execution module, optimizes control variables such as thrust and rudder angle through rolling optimization based on the USV's three-degree-of-freedom nonlinear dynamics model. This study constructs a closed-loop "perception-decision-execution-learning" paradigm and employs gradient pruning and a customized reward function to ensure the stability of algorithm training and the optimality of control decisions. Lateral deviation and heading angle error are used as evaluation metrics to verify the control performance. Simulation results show that this method effectively solves the adaptability challenge of traditional control strategies in complex environments. Compared with the traditional ALOS-PID method, the average lateral deviation is reduced by 37% and the heading angle error is reduced by 21%, thus realizing high-precision path tracking control for unmanned surface vessels and providing a new method for autonomous surface vehicle navigation.

This paper builds on the Water-Energy-Food-Ecosystems (WEFE) nexus approach through insights gleaned from interviews with project coordinators involved in nexus-oriented research and initiatives. By employing a structured interview format, empirical evidence highlighting the perspectives of experts on the methodologies, practices, and challenges within the nexus framework was gathered. The findings indicate that, despite growing understanding of the complexities of the WEFE nexus, its translation into practice remains limited across many project contexts. Interview participants emphasized the importance of recognizing the interdependencies among water, energy, food, and ecosystems to enhance resource efficiency and resilience against climate change. Key opportunities identified include the development of innovative technologies and nexus-specific indicators aimed at improving nexus understanding, fostering stakeholder engagement, and supporting collaborative decision-making. The study also identified pressing challenges, such as data availability, methodological alignment, and the integration of nexus activities with broader policy frameworks. Overall, the analysis examines how the WEFE nexus is conceptualized and operationalized in practice, and how analytical and governance-related challenges shape its implementation in European research and innovation projects. The insights gained from this research underscore the need for enhanced governance, effective communication, and strategic collaboration among stakeholders to capitalize on the full potential of the WEFE nexus. This paper contributes to the discourse on integrated resource management by providing a real-world perspective on the application, efficacy, and impact of the WEFE nexus framework, thereby enriching the existing knowledge stock on sustainable resource utilization.