Coastal wetlands have important economic and ecological value. Rapid and accurate monitoring of the status of coastal wetlands is of great significance for the protection and management of coastal wetland resources. Due to factors such as the variability of the tide-level changes, similarity of vegetation spectra, and frequent cloud cover, remote sensing monitoring of coastal wetlands faced certain challenges. In this paper, we proposed a multi-technology coupled remote sensing classification method of coastal wetlands that considers tide-level changes and vegetation phenological characteristics. Based on the Google Earth Engine (GEE) platform, the Fmask (Function of mask) algorithm was first performed for cloud testing and cloud removal processing. Then, the S-G (Savitzky-Golay) filtering algorithm was used to reconstruct NDVI time series data and extract vegetation phenological characteristic parameters. In this phase, the random forest algorithm was applied for the classification of four vegetation types namely Phragmites australi, Suaeda salsa, Spartina alterniflora, and Imperata cylindrical. Finally, the Maximum Spectral Index Composite (MSIC) algorithm was used to generate composite images of the highest and lowest tide levels. The tidal flats and seawater were precisely extracted using the Otsu algorithm based on these two composite images. Combining these feature types, the refined remote sensing classification of coastal wetlands was ideally obtained. The results showed that start-of-season time, end-of-season time, length of season, base value, amplitude, and small seasonal integral were the six key vegetation phenological characteristic parameters for distinguishing different types of coastal wetland vegetation. Applying this method to classify coastal wetlands on the Yancheng coast, the overall classification accuracy was 96.50%, and the Kappa coefficient reached 0.957 1. Among the wetland vegetation, the highest user accuracy was 96.59% for Spartina alterniflora, followed by P. australi and Suaeda salsa, and the lowest was 93.55% for Imperata cylindrical. Compared with object-oriented methods, our method can extract the complete range of tidal flats, and the overall accuracy is improved by 10.25%, reflecting the potential application of vegetation phenological characteristics in remote sensing monitoring of dynamic changes in coastal wetlands.

The “fairy circle” represents a unique form of spatial self-organization found within coastal salt marsh ecosystems, profoundly influencing the productivity, stability, and resilience of these wetlands. Unmanned Aerial Vehicle (UAV) imagery plays a pivotal role in precisely pinpointing the “fairy circle” locations and deciphering their temporal and spatial development trends. However, identifying “fairy circle” pixels within two-dimensional images poses a considerable technical challenge due to the subtle differences in color and shape characteristics between these pixels and their surroundings. Therefore, intelligently and accurately identify “fairy circle” pixels from two-dimensional images and form individual “fairy circle” for the identified pixels were the current technical difficulties. This paper introduced an innovative approach to extract “fairy circle” from UAV images by integrating the SAM (Segment Anything Model) visual segmentation model with random forest machine learning. This novel method accomplished the recognition and extraction of individual “fairy circle” through a two-step process: segmentation followed by classification. Initially, we established Dice (Sørensen-Dice coefficient) and IOU (Intersection Over Union) evaluation metrics, and optimize SAM’s pre-trained model parameters, which produced segmentation mask devoid of attribute information by fully automated image segmentation. Subsequently, we aligned the segmentation mask with the original image, and utilized RGB (red, green, and blue) color channels and spatial coordinates to construct a feature index for the segmentation mask. These features underwent analysis and selection based on Out-of-Bag (OOB) error reduction and feature distribution patterns. Ultimately, the refined features were employed to train a random forest model, enabling the automatic identification and classification of “fairy circle” vegetation, common vegetation, and bare flat areas. The experimental results show that the average correct extraction rate of “fairy circle” is 96.1%, and the average wrong extraction rate is 9.5%, which provides methodological and technological support for the accurate depiction of the spatial and temporal pattern of “fairy circle” as well as the processing of coastal remote sensing images by UAVs.

In this paper, a high spatial-temporal resolution sea ice concentration estimation method for the Arctic melting season is proposed, aiming to improve the overestimation of sea ice concentration in seawater by the Global Navigation Satellite System-Reflectometry (GNSS-R). The method utilizes machine learning algorithms to extract feature parameters from the Delay Doppler Maps (DDM) obtained through GNSS-R and combines them with sea surface temperature data to establish a LightGBM model. The inversion results are then subjected to correlation analysis and evaluation against reference sea ice concentration values. The model’s performance is compared with the sea ice concentration product from OSI SAF, demonstrating good consistency, with correlation coefficient, mean absolute error, and root mean square error being 0.965, 0.061, and 0.090, respectively. This approach enables high-precision estimation of sea ice concentration in the Arctic marginal ice zone.

Plastic floating objects have a profound impact on the marine environment. The nearshore process of the floating objects is mainly influenced by the action of waves. On the kinetic characteristics of plastic floating objects, previous studies were not thorough for the nearshore regime. In this paper, laboratory experiments were used to study the drift-law of plastic-floating objects under finite-water-depth waves. The relationship between the horizontal drift velocity of a weakly inertial plastic blocks and their characteristics, along with the wave steepness were discussed. The experimental results show that the drift of plastic blocks is affected by Stokes drift and Euler return flow, which is in good agreement with the second-order Lagrange drift theory. As the floating object’s size is much smaller than the wave length, size or density of the floating objects has no significant effect on drift. The drift of floating objects is proportional to the square of wave steepness. Based on the experiments conducted in this study and previously published experimental data, the empirical formula is revised to provide useful reference for the nearshore migration law of plastic floating objects and so for the relevant prediction.

This study used ERA5 reanalysis data to collect an extreme wave event dataset for various regions in the Arctic Ocean during August to October from 1979 to 2021. The analysis focused on the frequency of extreme wave events, changes in extreme wave heights, features of wave power and wave direction distribution, as well as the change of sea ice during wave events. The results suggest that as sea ice decreases, the range of extreme wave activity in the Arctic expands. All regions, except the Barents Sea, exhibit an increase in the occurrence of extreme wave events. In particular, extreme wave heights in the East Siberian Sea and Laptev Sea have significantly increased at rates of approximately 3.5 cm/a and 2 cm/a, respectively, with event frequency reaching around 4 events per year. The dominant wave direction in the Laptev Sea is southerly, facilitating more frequent wave propagation into the ice zone compared to other seas, with an average wave energy flux ranging from 5−8 kW/m. The changes in sea ice within extreme wave events primarily occur in the marginal ice zones and are associated with wind direction: sea ice is more likely to decrease with on-ice winds, while it is more likely to increase with off-ice winds.

Submesoscale processes associated with strong vertical velocities play significant roles in the vertical transport of tracers between the ocean surface and the interior, including heat, buoyancy, and mass. Based on the results of the (1/48)° LLC4320 model, this study investigates the seasonal variations of submesoscale vertical heat transport in the Kuroshio Extension. The results show that submesoscale vertical heat transport in the Kuroshio Extension exhibits distinct seasonal variations, with strong transport in spring and winter, and weaker transport in summer and autumn. The variation of net submesoscale vertical heat flux in the upper ocean is consistent with the trend of mixed layer depth, which shows overall upward submesoscale heat transport above the mixed layer and strong alternating positive and negative submesoscale vertical heat transport below the mixed layer, resulting in relatively small net submesoscale vertical heat transport. Coherent spectral analysis of vertical heat flux wavenumber-frequency suggests that submesoscale vertical heat transport below the mixed layer may be caused by linear internal waves, but the upward and downward vertical heat transports induced by linear internal waves counteract each other, leading to a reduced net vertical heat transport after averaging over the season.

This study focuses on the physical process of a sea fog event during Typhoon “Lekima” (1909) in the northern Yellow Sea by using observation data, reanalysis data and backward trajectory model. The analysis indicates that the typhoon circulation was the decisive factor determining whether fog formed offshore and developed inland. The warm and humid southerlies from the South Yellow Sea condensed into fog on the colder sea surface besides the typhoon center, which not only provided sufficient moisture for the formation and development of the sea fog but also formed a significant inversion layer over the fog area with the downdraft in the center of the typhoon. The “stable up and turbulent down” structure in the atmospheric boundary layer improved the development of sea fog on the coast and inland area. However, the horizontal wind steering and the strengthening wind speed behind the typhoon strengthened the wind shear in the atmospheric boundary layer, resulting in the enhanced turbulent mixing and the decrease of the stability in the bottom atmospheric boundary layer, which was the main cause of the fog dissipation.

Near-inertial motion is a type of motion in the ocean that is ubiquitous and has a frequency close to the local inertial frequency. Tropical cyclones are one of the mainmechanisms that generate near-inertial motion. This study established a three-dimensional hydrodynamic model based on COAWST (Coupled Ocean-Atmosphere-Wave-Sediment Transport) numerical model system, which couples waves and currents, covers the northern shelf of the South China Sea, and was fully verified. The model was used to simulate the near-inertial motion triggered by Typhoon “Cempaka”, the No.7 typhoon of 2021, on the shelf of western Guangdong. The results indicate that there are spatially two peaks of near-inertial kinetic energy, one in the coastal area with the highest typhoon wind speed, and the other at 130 km offshore, with the second energy peak lasting longer. In the coastal area with water depth shallower than 40 m, the near-inertial motion is mainly in a barotropic mode. As the water depth gradually increases offshore, we found that the near-inertial motions exhibit a clear two-layer structure inthe regions with depths ranging from 70−100 m, with opposite directions of near-inertial flow in the surface and bottom layers, and two energy peaks in the vertical direction, showing the characteristics of the first baroclinic mode. Through dynamic modedecomposition, we found that some areas with obvious two-layer structures are composed of the first and secondbaroclinic modes. As the water depth continues to increase, higher modes of near-inertial flow account for an increasing proportion of the total near-inertial kinetic energy. Momentum balance analysis shows that in the coastal area with shallow water depth and high wind speed, the balance of momentum equation in the entire water layer is dominated by the vertical turbulent viscous force and pressure gradient force. In offshore areas with deeper water depths and lower wind speeds, vertical turbulent viscous forces are concentrated in the surface and bottom layers, and the balance of momentum equation in the intermediate water body is mainly dominated by the pressure gradient forces, Coriolis forces, and local acceleration. This indicates that the near-inertialmotion in the coastal area is mainly driven by barotropicwave caused bywind stress, while in the continental shelf area, the near-inertial motion in the uppermixed layer is driven by wind stress, and the near-inertial motion below the mixed layeris driven by barotropic pressure gradient force.

The annual, seasonal, monthly and diurnal variations of sea surface wind field over the Taiwan Strait were analyzed based on the new version of Cross-Calibrated Multi-Platform Version 3.1 (CCMP V3.1) wind data from 1993 to 2022. The results showed that the wind field in the Taiwan Strait and the water around Taiwan Island had obvious spatial distribution characteristics, the topographic effect leads to the maximum and minimum wind speed regions in different sea areas. Because the central Taiwan Strait was affected by the “narrow tube effect”, the wind speed was the highest and the wind direction was basically parallel to the strait in winter; the wind speed in summer was lower than the speed outside the channel, and there was no “narrow tube effect”. In addition, the sea surface wind field also had obvious seasonal and monthly variation characteristics. The northeast wind prevailed in winter, and in this season the wind speed was the highest in the whole year; the southwest wind prevailed in summer with the lowest wind speed; the characteristics of spring and autumn monsoon fields were similar, both prevailing northeast wind; winter monsoon last longer than summer monsoon, accounting for about three quarters of the year. The analysis of the inter-annual variation of wind field showed that the wind direction tended to deflect at a large angle in summer. The annual mean wind speed maintained a basically flat linear trend, and the abnormal high or low in some years was related to the occurrence of El Niño-Southern Oscillation (ENSO). When the diurnal variation characteristics were studied, it was found that the wind speed and direction fluctuated most at 20 PM. The wind speed varied periodically within a day. The diurnal variation of wind direction deflection was most obvious in summer.

The impact of the El Niño-Southern Oscillation (ENSO) on the climate of the low-latitude tropical region of the Malay Peninsula, particularly with regard to precipitation, remains a topic of debate. This study focuses on the NTT-3 drill core from Setiu Lagoon in Terengganu, northeastern Malay Peninsula. By employing analyses such as grain size, total organic carbon/nitrogen content, C/N ratio, and XRF core scanning, this research investigates the sedimentary environmental changes in the drill core and their response to ENSO. The results reveal two distinct trends in the drill core record, appearing around 1970 (at 84 cm depth). Sediment characteristics such as grain size and geochemical features of both organic and inorganic components suggest the possible occurrence of episodic sedimentation or sedimentary interruptions, with exceptionally slow sedimentation rates observed in the lower part of the core before 1970. Since 1970, the organic components in the lagoon sediment primarily originate from mangroves, accompanied by contributions from freshwater phytoplankton associated with river inputs. Spectral analysis indicates a pronounced ENSO periodic variation in the upper part of the drill core since 1970. The variations in Zr/Rb and Zr/Ti ratios correlate well with the occurrences of strong El Niño and La Niña events. This conclusion not only supports contemporary observations of climate change in the eastern coastal region of the Malay Peninsula but also provides direct geological evidence of ENSO variations in the sedimentary record. This discovery holds significant practical implications for a comprehensive understanding of the impact of ENSO on climate change in Asia, regional land-sea interaction processes, and environmental responses.