Caribbean Small Island Developing States (SIDS) are on the frontline of climate-induced coastal risks, where rising sea levels and intensifying storm surges converge with concentrated socioeconomic exposure. The inherent spatial constraints, economic dependency on tourism, and infrastructure clustering in coastal zones exacerbate systemic vulnerability. This study establishes an integrated high-resolution compound coastal risk framework by coupling hydrodynamic storm surge simulations with sea level projections and socioeconomic exposure data. The framework evaluates systemic risks across diverse return periods and Shared Socioeconomic Pathways including SSP1-1.9, SSP2-4.5, and SSP5-8.5, utilizing a modified static inundation model to integrate sea level rise and surge extremes for generating spatially explicit vulnerability assessments. Results revealed spatial heterogeneity and a distinctive ‘high-exposure–high-density–high-sensitivity’ configuration across several SIDS. Under SSP5–8.5, annual economic losses exceed USD 300 million in Jamaica and Cuba, and over 30,000 people may be affected in Dominica. Mitigation pathways consistent with a 1.5 °C warming limit (SSP1–1.9) reduce potential losses by 30–50%, while adaptation strategies, such as coastal ecosystem restoration and early-warning systems, deliver up to 40% carbon co-benefits. Beyond risk quantification, this framework supports climate-resilient infrastructure planning. By correlating spatial risks with adaptation finance, this research supports the Paris Agreement and Sustainable Development Goals (SDGs).

Lakeshores are both vital components of lake ecosystems and non-renewable resources that support human development. Understanding their utilization is essential for assessing human–lake interactions, yet accurate lakeshore type identification remains challenging because of the complex water–land interfaces and diverse natural and anthropogenic contexts of lakes. To address this, we developed RoLaSTIM (Robust Lake Shore Type Identification Method), an operational approach for classifying lakeshores as natural or artificial based on spatial analysis and land cover conditions. RoLaSTIM was applied to 12 typical global lakes, and their annual Shore Utilization Rates (SURs) were analysed from 2000 to 2022. RoLaSTIM achieved high classification accuracy (area-adjusted overall accuracy: 98.93 ± 0.02%, weighted F1 score: 98.94%), benefiting from a two-level buffer strategy and corresponding physical rules that minimize misclassification. Key factors influencing accuracy included the lake SUR, artificial lakeshore length, land cover complexity, and satellite image resolution. Moreover, we identified four distinct SUR evolution trends: increasing, stable, inverted (‘∩’-shaped), and fluctuating (‘M’-shaped). RoLaSTIM is suitable for robust and consistent lakeshore classification at large scales, supporting efficient SUR assessment and long-term monitoring. This method fills a critical gap in global lakeshore research and offers valuable insights for lakeshore management, conservation, planning, and sustainable development.

Enhanced urban CO2 monitoring and understanding its spatiotemporal patterns and driving factors are essential for emission management and climate change mitigation. This study developed a high-resolution framework for predicting and mapping CO2 concentrations within the road network of Shenzhen by integrating vehicle-cruising observations, street-view panoramas, and multisource remote sensing data. The proposed machine learning model demonstrated strong predictive performance (R² = 0.92). Furthermore, the effects of urban function, urban development intensity, traffic conditions, and environmental factors on CO2 concentrations were systematically examined using explainable machine learning techniques. The results indicate that in urban centres, human activities exert a substantial influence on CO2 levels. Effective non-motorway planning, accessible public transport systems, and diversified urban functions are associated with lower CO2 concentrations. Notably, areas with high vegetation cover were also found to exhibit elevated CO2 concentrations, and the influence of greenery on Shenzhen's CO2 levels was positive in November. By integrating multisource data from the perspectives of the urban landscape and street configuration, this study provides an interpretable approach for analysing the complex drivers of urban CO2 dynamics. Overall, the findings establish a cost-effective methodological framework for refined urban carbon monitoring and offer actionable insights to support low-carbon urban planning and evidence-based policy formulation.

ABSTRACT Accurately identifying extreme land surface temperature (LST) zones—heat cores (HCs, areas with persistent high temperatures) and cold cores (CCs, areas with persistent low temperatures)—is crucial for urban climate resilience. Traditional approaches fail to capture subtle LST variations and hierarchical structures. This study introduces the Local Contour Tree Analysis (LCTA), a graph theory–based method that captures topological hierarchies and dynamic interactions, surpassing conventional fixed-threshold techniques. Applied to western Shenzhen, China, it revealed HCs' northward interannual migration from southern clustering through dispersion to northern clustering, while CCs remained stable as primary cooling sources. Seasonally, HCs exhibited stronger clustering in summer and greater dispersion in winter; CCs maintained structural layering year-round with reduced heterogeneity in winter. Structurally, HCs were simple and compact, while CCs were complex, multi-layered, and interconnected. Urban thermal deterioration concentrated in stable HCs due to impervious surface expansion and vegetation loss, while improvements occurred around CCs through vegetation recovery. These shifts reflected urbanization trends and revealed scale-specific characteristics tied to development stages. Findings elucidate HC and CC mechanisms, informing green-blue infrastructure planning via targeted vegetation corridors to enhance cooling connectivity and counteract HC expansion, offering actionable heat mitigation strategies for rapidly developing urban regions.

ABSTRACT The littoral zone is crucial in the aquatic ecosystem of lakes, and water depth is often insufficient when using the traditional zigzag echo-sounding method. This study evaluated the applicability of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data for lake water depth reconstruction on the Tibetan Plateau (TP). The ICESat-2 photon effectively penetrated shallow water and showed the underwater topography clearly. The water depth at the 549 intersection points across the 33 lakes ranged from 0.4 to 19.25 m, with an average of 4.62 m, and the RMSE of water depths from the ICESat-2 data was 0.42 m comparing with 33 lakes with in situ bathymetric data. The ICESat-2 data since 2018 covered 1392 large lakes, and the total observation days for 677 lakes was >20 days. Water transparency affects the accuracy of water depth reconstruction. This research suggests that ICESat-2 data has substantial potential for reconstructing lake water depth on the TP, which is useful for lake water storage estimation and water resource management.

Remote sensing techniques typically capture flood extent at discrete time points but cannot directly estimate flood depth. Social media, as an emerging source of volunteered geographic information (VGI), provides real-time visual evidence of fine-grained flood severity. However, this potential remains underexplored. In this study, we propose Social-VFG, an interpretable framework for assessing road inundation using vehicle wheels observed in images. To avoid training annotation-intensive wheel segmentation models, wheel contours above floodwater are efficiently extracted by coupling a trained wheel detector with the Segment Anything Model (SAM). Based on these contours, an interpretable rule-based indicator is proposed to characterize wheel inundation status as water levels rise. To mitigate the indicator’s sensitivity to segmentation noise and scale variations, four complementary features are further integrated into a random forest classifier to improve the robustness of inundation classification. Using the 2023 Zhuozhou flood as a case study, road inundation risk maps derived from Weibo images show strong spatial consistency with flood extents extracted from GF-3 SAR imagery. Importantly, social media observations provide fine-grained information on flood severity and effectively compensate for spatial gaps caused by satellite imagery limitations. The resulting road inundation risk maps can support flood-aware route planning under extreme rainfall conditions.

Internal solitary waves are rapidly propagating disturbances that pose hazards to maritime operations while contributing to marine ecosystems through nutrient uplift. Most existing internal solitary wave detection approaches are trained and validated under low-to-moderate sea states, where surface manifestations remain visually discernible. Under high sea states, however, wind–wave clutter substantially masks internal solitary wave induced roughness modulations, resulting in persistent under-detection and biased climatological assessments. To overcome this limitation, ST-Multi-ISWNet is proposed. The principal innovation lies in the joint design of two components: first, an adaptive spectral extraction module that disentangles internal solitary wave signals from clutter in the wavenumber domain; second, a cross-attention multimodal attention mechanism that explicitly conditions instance segmentation on spatiotemporal environmental forcings. Evaluations conducted on datasets acquired under high sea states demonstrate that ST-Multi-ISWNet consistently outperforms existing segmentation models, achieving improvements of 11% in accuracy and 10% in mAP50. ST-Multi-ISWNet offers an effective framework for more comprehensive detection of internal solitary waves under high sea states. The results indicate that 24.15% of internal waves in the South China Sea during winter were underestimated in previous studies. In addition, the detection outcomes are cross-validated using SWOT data.

ABSTRACT The interactions between scalar and vector properties is vital in oceanic phenomena and energy/material circulation processes. This study integrates scalar and vector fields in ocean visualization to clarify their interplay and proposes a standardized approach for designing transfer functions that simplify ocean feature extraction. An interactive collaborative transfer function operational mode was introduced to visualise the scalar and vector fields, effectively capturing the dynamic characteristics of oceanic phenomena under mutual influence. Using the high-resolution GLORYS12V1 marine dataset, we visualized thermohaline fields and currents. Results show that the collaborative visualisation method and standardized transfer function effectively extract morphological outlines of ocean fronts and mesoscale eddies. Furthermore, the real-time collaborative transfer function reveals high spatial and directional consistency between ocean fronts (extracted via a 0.05 °C/km temperature gradient threshold) and Kuroshio axes (identified using a 0.1 m/s flow velocity threshold), which highlights the barrier effect of ocean fronts on meridional heat exchange. Additionally, we revealed the dynamic entrainment and transport capability of energy and material in mesoscale eddies. The above results demonstrate that the proposed synergistic transfer function in this study can effectively uncover the key role of scalar-vector field interactions in the evolution of oceanic phenomena.

ABSTRACT To provide high-quality data support for agricultural policy-making and grain subsidies, this study presents an efficient method for extracting winter wheat planting areas using Landsat series satellite imagery and monthly maximum normalized difference vegetation index (NDVI) stacks. Three ensemble decision-tree algorithms—Random Forest, XGBoost, and CatBoost—were compared. The best model achieved 91% cross-validation accuracy, with a strong statistical validation accuracy in terms of its municipal-scale validation R2 = 0.91 (MAE = 49,650 hm2; RMSE = 64,440 hm²) and county-scale R2 = 0.84 (MAE = 7125 hm2, RMSE = 9875 hm2). A spatiotemporal analysis revealed a notable decline in winter wheat area in Shandong Province, which was concentrated in its western and southern regions. The cultivation centre shifted westwards, and then northwards, and the landscape patterns transitioned from large, aggregated patches to small, dispersed patches. This trend of decreasing intensification level for the winter wheat planting regions poses challenges for achieving water-saving and intensive land management goals. These trends are influenced by climate, topography, and socioeconomic factors, as well as agricultural policies. The proposed method offers robust support for attaining large-scale crop monitoring and sustainable agricultural management.

ABSTRACT Against rapid urbanization, ensuring carbon emission equity is critical for inclusive sustainable development. Using Beijing's smart card data, we develop a bottom-up accounting model to quantify travel carbon emissions in public transportation at the Traffic Analysis Zone scale, assessing equity through three dimensions: vertical (Palma ratio), horizontal (Gini coefficient), and spatial (Carbon Burden Index). Results show that the stratification and efficiency differences in public transportation services drive emission characteristics. Subway travel time is approximately twice that of buses, and the travel distance is 2.5–3.4 times longer, resulting in 80.9% higher per capita emissions. Weekdays, weekends, and holidays reflect distinct carbon emission patterns driven by ‘commute-driven,’ ‘mixed demand,’ and ‘leisure-oriented’ travel behaviors, respectively. During weekday peak periods, low-income groups bear nearly half of the travel emission burden, with per capita emissions 69% higher than those of high-income groups. Marginalized groups bear hidden environmental costs for urban economic efficiency. Commuting demand and jobs-housing spatial mismatches exacerbate equity disparities. Substituting short trips with shared bikes reduces emissions by 2%. The study reveals the socioeconomic and spatial differentiation mechanisms of urban public transportation travel carbon emissions. These findings provide a scientific basis for formulating low-carbon transportation policies that balance equity and efficiency.