Ocean data buoys are a critical means of automatically acquiring offshore oceanographic and meteorological data, offering advantages of long-term, fixed-position, continuous, and real-time monitoring. The turbulence generated by the buoy’s structure is a significant factor affecting wind speed measurement accuracy. In this study, a 10 m-diameter buoy was analyzed to evaluate the influence of structural turbulence and shielding effects on wind speed measurements. The Reynolds-averaged Navier-Stokes (RANS) equations, combined with the RNG k–ε turbulence model, were employed to simulate the flow field and turbulence characteristics. Numerical simulations were conducted to calculate wind fields around the buoy and measured wind speeds at two sensor heights with varying pitch angles. The shielding effects on wind measurements were examined across different wind directions. To assess the impact of photovoltaic panels, wind fields and wind speed measurements were also analyzed for a configuration without these panels. Results indicate that shielding effects can cause substantial wind speed measurement errors, particularly when sensors are located on the leeward side, due to the formation of low -wind -speed zones in the wake region. Measurement error increases with higher incident wind speeds. Sensors positioned at greater elevations exhibit improved accuracy, as they are less affected by near-surface turbulence. Removing photovoltaic panels reduces measurement error; however, the shielding effects caused by the buoy body itself remain significant and cannot be neglected.

The validation of satellite-derived chlorophyll-a concentration (Chla(sat)) products is crucial for ocean colour remote sensing applications. The rapid development and deployment of Biogeochemical Argo (BGC-Argo) floats have provided a large amount of time-series Chla vertical profile data for validation. When only using the surface Chla (average within top 10 metres, Chla(10 m)) as the 'ground truth' value, the validation in the open ocean of Eastern Pacific indicates Chla(sat) to be significantly overestimated, particularly in the region with low Chla10 m ( < 0.1 mg m-3), and the mean absolute percent difference (MAPD) peaks at 170%. The nonuniform vertical distribution of Chla was found to be mainly responsible for this high uncertainty. The more prominent the nonuniform vertical distribution (such as a high ratio of deep chlorophyll-a maximum (DCM) to Chla(10 m) and shallow mixing layer depth (MLD)), the higher the difference between Chla(sat) and Chla(10 m). Radiative transfer simulations of remote sensing reflectance (R-rs) confirmed these findings and demonstrated that Chla distribution below the DCM depth had little impact on R-rs. Unlike previous studies relying on surface Chla or fixed-depth averages, we proposed the average Chla within the DCM depth (Chla(DCM)) as the more suitable 'ground truth' value, such that the MAPD between Chla(DCM) and Chla(sat) was only 31.77%, satisfying the IOCCG requirement of < 35%. This indicates that Chla(sat) cannot be simply regarded as surface Chla but reflects the vertical distribution of Chla within the DCM depth, which should be advantageous for a more accurate evaluation of marine primary productivity in the open ocean when using Chla(sat) products.

Continuous monitoring of tidal currents with high spatiotemporal resolution is essential for navigation safety and marine environmental forecasting in strait regions. This study proposes a novel method for estimating diurnal tidal currents by combining Scharr operator-enhanced sea surface temperature (SST) imagery with the wavelet-based optical flow velocimetry (wOFV) algorithm. Using hourly 1-km resolution JCOPE-T 1 ks SST reanalysis data over the Western Channel of the Korea Strait, we retrieved continuous surface currents across four representative months in 2024. The Scharr operator significantly improved retrieval accuracy by enhancing thermal front features, increasing the U component correlation coefficient from 0.379 to 0.446 relative to raw SST, with improvements most pronounced in weak-gradient regions. Compared with the TPXO tidal model, the proposed method showed better agreement with high-frequency radar observations, reducing mean absolute errors (MAE) values for the U and V components by 12.2% and 20.9%, respectively. Harmonic analysis of the retrieved currents confirmed the M2-dominated mixed tidal regime. The tidal ellipse parameters showed good agreement with radar observations (M2 semimajor axis MAE of 0.069 m/s, inclination MAE of 8.98 degrees). The pronounced rectilinear flow characteristics reflect strong topographic constraints on tidal wave propagation. After removing tidal signals, the residual current clearly revealed the northeastward transport of the Tsushima Warm Current. In addition, a cyclonic eddy (similar to 20-30 km diameter) was successfully identified during tidal transitions, demonstrating the method's potential for detecting fine-scale dynamical processes. This approach provides a new pathway for all-weather, high-resolution tidal current monitoring in strait regions, serving as a valuable complement to existing observation systems and numerical models.

To address the requirements of two-dimensional (2D) low-frequency vibration monitoring and sensor miniaturization, this paper proposes a compact 2D fiber Bragg grating (FBG) acceleration sensor with four identical cantilever beams. The sensor uses four identical cantilever beams as elastic sensing elements. By applying differential signal processing to the variations in the central wavelength of the FBGs, it suppresses temperature cross-sensitivity and enhances measurement sensitivity. The experimental results demonstrate resonant frequencies of 500 (X-axis) and 525 Hz (Y-axis), with stable operation in the 10-200 Hz range, sensitivities of 38.01 and 38.10 pm/g with excellent linearity (r2>0.99), crosstalk ratios of 11.6% and 13.7%, and differential temperature sensitivities of only 1.7-2 pm/°C. Owing to its miniaturized structure and consistent bidirectional sensitivity, the sensor is highly suitable for two-dimensional low-frequency vibration monitoring in space-constrained environments and in scenarios with strong electromagnetic interference (EMI).

The advancement of marine fishery constitutes a vital element in the endeavor to build a maritime power and safeguard food security. In light of the strategic requirements of the national marine fishery and marine ranching, In this article, three pivotal aspects are focused on: marine fishery environmental monitoring systems, core monitoring equipment, and key monitoring techniques. A feasibility assessment of the integrated implementation was conducted, probing into the interplay between the development of the marine economy and the marine fishery environment. Notably, the significance of establishing a sound marine fishery environmental monitoring system was underscored. Through in-depth analysis and systematic collation, a comprehensive understanding of the status of demands, industrial policies, technical capabilities, and future trends in the global marine fishery environmental monitoring system was analyzed. In addition, the core challenges and strategic approaches confronting the development of China’s marine fishery environmental monitoring system were clarified. In the near future, by practical development requirements, an ecosystem with monitoring platforms and sensors will be gradually established. Novel organizational models will be explored with key technologies, and major scientific schemes and core tasks in this field will be put forward. In combination with relevant policies regarding fishery environmental monitoring, constructive suggestions for industrial development were provided, and future direction for its progress was outlined.

Marine biofouling poses a significant challenge for ocean engineering, with microbial biofilm formation being a critical prerequisite for macrofouling colonization. Traditional chemical antifouling strategies face increasing restrictions due to ecological toxicity and microbial resistance, creating an urgent demand for eco-friendly physical alternatives. This study aimed to systematically compare the wavelength-dependent effects of 405 nm and 450 nm antimicrobial blue light on Halomonas pacifica biofilm formation. Biofilms were constructed on glass slides and continuously irradiated at 50 mW/cm2 for 4 days (cumulative radiant exposure about 17,280 J/cm2), with a non-irradiated control. Total biomass was quantified by crystal violet staining, three-dimensional morphology was analyzed by white light interferometry, and bacterial viability was visualized by confocal laser scanning microscopy with DMAO/PI staining. The 450 nm treatment significantly reduced total biofilm biomass by 20.9% (P < 0.05), while 405 nm decreased biomass by 15.5% without statistical significance. In contrast, 405 nm strongly suppressed vertical growth, reducing average dry thickness by 77.2% (P < 0.01) and arithmetic mean roughness (Ra) by 55.0% (P < 0.01), whereas 450 nm optimized overall micromorphology by reducing maximum height (Sz, P < 0.05). Confocal imaging revealed dense continuous biofilms in controls, sparse fragmented structures in the 450 nm group, and intermediate density in the 405 nm group. These results indicate that the two wavelengths regulate biofilm formation through distinct mechanisms: 450 nm excels in inhibiting proliferation and reducing biomass, while 405 nm is superior in suppressing vertical thickening and minimizing attachment sites for subsequent macrofouling. This study demonstrates that 405 nm and 450 nm blue light are promising complementary strategies for eco-friendly marine antifouling.

For addressing difficult detail extraction and low operating efficiency in monitoring sea ice in a large area with wide-field-of-view images from the Chinese Gaofen-1 satellite, a lightweight, high-precision sea ice segmentation network adaptive multistatistic fusion attention (AMFA) module using DeepLabV3+ as the base architecture (AMFA-DeepLab) is proposed. First, the module replaces the backbone network with a lightweight MobileNetV2 to ensure feature extraction capability and greatly reduce model computational complexity using inverted residuals and depthwise separable convolution. Second, to solve the problems of fragmented ice texture blurring and speckle noise interference in optical images, an AMFA is designed and introduced into the decoder side. This module innovatively integrates the global median pooling branch and adapts the recalibrated feature weight through a dynamic channel mixing mechanism, effectively enhancing the model’s capability of capturing fine sea ice edge features and its antinoise robustness in complex backgrounds. Experimental results based on the dataset from Liaodong Bay in the Bohai Sea of China show that the intersection over union of AMFA-DeepLab reaches 92.15% and the F1-score reaches 95.91%, increases of 3.06%, and 1.68%, respectively, compared with those of the baseline model. In addition, only 5.85 million model parameters are needed, the training time is shortened to 4.42 h, and the inference speed is 281.76 frames per second. Visualized analysis and generalization test further demonstrates that this model can accurately eliminate clutter interference from coastal land and seawater and extract the fine filamentous structure of drift ice in the scene of complex melting ice. This research overcomes the precision bottleneck while achieving an ultimate lightweight model, providing efficient technical support for operational dynamic monitoring of sea ice disasters based on Chinese GaoFen-1 satellites.

Spatial-spectral fusion offers a viable solution for the quantitative inversion of water parameters using multispectral resolution images (MSRIs) and limited bands of high spatial resolution images (HSRIs). Most existing fusion methods assume that the ground coverage type at the same location or the spatial patterns of images do not change over time. However, these fundamental assumptions are not valid under highly dynamic ocean conditions caused by various currents and tides. In this study, we propose a new assumption: the types of optical water bodies remain consistent within a certain time frame and a specific spatial region, and the spectral characteristics of each optical water type remain stable. Subsequently, a new spatial-spectral fusion method, referred to as the optical water classification based data fusion (OWCDF), was developed to realize accurate spatial-spectral fusion in oceanic environments. The OWCDF algorithm comprises three key steps: 1) recalibration based on a maximum cosine correlation (MCC) match, 2) spectral regression based on optical water classification, and 3) residual compensation. A well-designed scheme was developed to evaluate the performance of OWCDF against existing algorithms by fusing a CZI/HY-1C/D image (HSRI) with time-series GOCI-II images (MSRI), with temporal differences increasing from 1 h to 4 h. The OWCDF algorithm exhibited a substantially better ability to resist the influence of highly dynamic changes in water bodies than other algorithms. Further tests applying OWCDF to the data of OLCI/S3 and CZI/HY-1C/D or CCD/HJ-2A/B confirmed its applicability to polar-orbiting satellites, achieving quantitative observation with a high spatial resolution even 2-3 times a day. In the future, the accuracy of the optical water type classification must be improved, and limitations under poor observation conditions, such as broken clouds and sun glints, should be further considered.

Mixed layer depth (MLD) and near-surface stratification (derived from bulk and subskin density) are crucial variables for defining stability within the upper ocean. Conventional paradigms generally describe the relationship between MLD and stratification for the subsurface layer or the upper ocean below 5 m, where a weak stratification is associated with a deeper MLD, and a strong stratification with a shallower MLD. In this study, satellite-based and in situ -based monthly data from 2011 to 2021 reveal a positive relationship between monthly-averaged near-surface stratification (above the bulk layer) and MLD. That is, on a monthly timescale, a weaker near-surface stratification co-occurs with a shallower MLD, and vice versa. This observed pattern deviates from the conventional expectation. Analysis shows that this phase synchronization is primarily driven by density variations in the bulk layer, which exceed those in the subskin layer. Further investigation using wind stress and buoyancy flux data indicates that MLD deepening aligns with enhanced stratification above the bulk layer. These findings refine the traditional understanding by emphasizing the distinct roles of vertical layers (subskin vs bulk). They underscore the necessity to incorporate layer-specific dynamics when using satellite-derived stratification to parameterize MLD dynamics in climate models. Such refinement is critical for optimizing predictions of ocean-atmosphere interactions and climate variability. Future work should leverage the fusion of satellite and in situ data to advance multi-layer ocean stability diagnostics.

Coastal water quality monitoring is pivotal for marine ecosystem management. However, current monitoring capabilities are constrained by a trade-off between the high accuracy of sparse in-situ sampling and the broad coverage but coarse resolution of traditional ocean color satellites. To bridge this gap, this study establishes a cloud-native framework coupling multi-source satellite fusion with machine learning to reconstruct a high-resolution (30 m) water quality dataset for the coastal waters of Guangdong, China, spanning a 40-year period. Leveraging the Google Earth Engine (GEE) platform, we harmonized Landsat-5/7/8 and Sentinel-2 MSI, processing over 22,172 valid scenes via a hierarchical regression strategy to overcome cross-sensor spectral inconsistencies. A Random Forest ensemble model was developed to retrieve Chemical Oxygen Demand (COD, R2 = 0.76), Total Nitrogen (TN, R2 = 0.86), and Total Phosphorus (TP, R2 = 0.88). The time series unveils a significant upward trajectory in COD concentrations (+4.08 & times; 10-3 mg/L/yr) with a regime shift around 2013. Spatially, COD plumes exhibited a "Westward Expansion" pattern during the wet season, contrasting with winter maxima for TN and TP. This parameter-specific divergence demonstrates that organic and nutrient pollutants are governed by different source and transport mechanisms-a finding that single-parameter studies would miss. We further explored the associations between water quality variations and environmental drivers, revealing a "Human-Climate Coupled" control pattern. Beyond this qualitative mechanism, we identify a dual-process pathway and quantify a 6-month lag between ENSO events and peak COD anomalies, moving the discussion from simple correlation to potentially predictive understanding. This study provides the first multi-decadal, high-resolution benchmark for the Greater Bay Area and demonstrates the value of cloud-based multi-sensor fusion for precision coastal management.

Atmospheric water vapor is a key variable controlling radiative transfer, the hydrological cycle, convective development, and ocean-atmosphere interaction. Conventional observations remain limited by sparse sampling, high deployment cost, cloud and precipitation sensitivity, or insufficient temporal continuity, especially over the ocean. Global Navigation Satellite System-based water vapor sensing provides all-weather, high-rate, and cost-effective measurements and can complement radiosondes, microwave and infrared sensors, reanalysis fields, and numerical weather prediction systems. This review synthesizes key technologies for marine environmental monitoring while distinguishing column-integrated precipitable water vapor retrieval from three-dimensional water vapor tomography. It addresses four questions: how zenith total delay is converted to precipitable water vapor; how fixed stations, regional networks, ships, buoys, and other kinematic platforms differ in retrieval strategy and error structure; which tomographic methods are demonstrated for regional three-dimensional moisture reconstruction and which remain future extensions for marine settings; and how multi-source fusion, artificial intelligence, and real-time precise products can support operational marine monitoring. Recent advances are synthesized in basic retrieval theory, processing software, land-based and network-based applications, kinematic marine platforms, tomography, validation, and assimilation. Remaining challenges include marine multipath, platform motion, ancillary meteorological information, limited open-ocean geometry, cross-sensor traceability, and standardized error models. Future development should emphasize coordinated sea-land-air observing networks, multi-antenna dynamic processing, physically constrained data fusion, and assimilation-oriented quality control.

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.

The relative reflectivity of the two orthogonal polarization modes in polarization-maintaining fiber Bragg gratings (PM-FBGs) critically determines their performance in applications such as PMF lasers and dual-parameter sensing, yet an effective method for tailoring this relative reflectivity is lacking. This paper presents a gradient coupling control model that can tailor the relative reflectivity of the two axes. We theoretically derive a relationship linking the coupling coefficient difference to the axial integral of the product of the ellipticity gradient and the energy gradient. Numerical simulations using the transfer matrix method are performed for four typical gradient distributions, and experiments are conducted by programming the motion trajectory of a vertical translation stage to achieve the desired ellipticity gradients. By combining vertical horizontal coordinated scanning with raised-cosine apodization energy modulation, we successfully achieve reflectivity differences of +4.4 dB (slow-axis higher), –4.6 dB (fast-axis higher), and values close to zero, corresponding to positive, negative, and zero integral conditions, respectively. The experimental results show consistent trends with the simulations, with only minor numerical discrepancies. This method provides a theoretical and experimental basis for the on-demand design of PM-FBGs with controllable reflectivity differences between the two polarization axes.

The study of stabilisation performance is a crucial consideration in the design of offshore floating platforms. For large floating structures, incorporating passive anti-roll tanks is a common technique for roll reduction. To investigate the feasibility of using ballast tanks for roll attenuation on the "Guo Hai Shi 1" platform, this study employs the internal tank theory to analyze the influence of four independent empty tanks, located around the platform, acting as anti-roll tanks with varying ballast water volumes. The results indicate that: (1) Different ballast water volumes within a single tank do not significantly affect the static stability parameters of the platform. (2) In regular wave simulations, the ballast tanks show limited effectiveness in reducing pitch motion along the wave incidence direction but effectively suppress coupled responses in other degrees of freedom. In the resonance case (Case 3), the minimum pitch occurs in Condition 1 at 10.56°, while the maximum pitch reaches 11.45° in Condition 2. Nevertheless, a 40% reduction in roll motion is achieved (3.36° in Condition 4 vs. 5.60° in Condition 1), along with a 24.5% reduction in yaw motion (39.22° in Condition 4 vs. 51.94° in Condition 1). (3) In irregular wave simulations, the ballast tanks effectively reduce the heave amplitude by up to 8.34% in sea state level 4 and 6.06% in sea state level 8, thereby enhancing its wave-following performance in the heave degree of freedom. (4) A CNN_BiLSTM_Attention algorithm is developed using hydrodynamic analysis generated datasets to predict the pitch motion time series of the platform under different ballast water conditions and sea states, while the model has a superior prediction performance (R² = 0.9658, RMSE = 0.5343, MAE = 0.3188, representing a 4.82% increase in R² and 30.31% reduction in RMSE compared to the original model). Future work will further explore the application of ballast tanks on floating platforms, with a focus on performance optimization and the development of advanced neural network models capable of predicting motion responses under various ballast configurations. Moreover, appropriate evaluation metrics will be established to assess the effectiveness of ballast tank designs. Efforts will also be directed towards integrating time-domain motion prediction using neural networks with control theories aimed at dynamically regulating ballast water volume to enhance platform stability.

In recent years, with the development of technologies such as the Internet of Things (IoT), big data and cloud computing, digital twin technology has gradually been applied in marine research. The digital twin realizes real-time monitoring, analysis and optimization of the state and behavior of a physical object or system by creating a virtual model. Research shows that digital twin technology has extensive application potential in ship design, marine resource development, marine equipment engineering design and optimization, marine ecological protection and early warning of disasters. Although digital twin technology has great potential in marine research, it also faces many challenges, including the complexity of data acquisition and processing, the accuracy and real-time performance of model construction, and the need for multidisciplinary cross-integration. An in-depth analysis of the technical bottlenecks and future development directions will provide an important reference for subsequent research and promote the further application and development of digital twin technology in marine research.

Missing satellite sensor scans or broken frames received by ground stations are frequent causes of missing irregular stripes in geostationary satellite imagery. In this paper, we propose an algorithm for recovering those missing pixels using the spatial and temporal correlation of continuous satellite observation data. Using the continuous satellite images as the reference map, and embedding the compression excitation module in the neural network model, the deep convolutional neural network model is established. The spatio-temporal nonlinear relationship from the consecutive times complete data of consecutive times are learned. Finally, the missing pixel values in the current satellite image are to achieve by combining the analysis of the time-series satellite image. Preliminary experiments show that the proposed method has the highest peak signal-to-noise ratio of 44.18 dB for the filled pixels when using the first four consecutive temporal images as the reference images. The mean absolute error, which is the lowest compared with the traditional spatio-temporal interpolation methods and other deep learning methods, reaches 0.50K, 0.58K and 1.12K for the clear-sky terrestrial area, clear-sky oceanic area, and the cloudy area, respectively.

Through active manipulation of wavelengths, a structure exposed to a water-wave field can achieve a target hydrodynamic performance. Based on the form invariance of the governing equation for shallow water waves, wavelength modulators have been proposed using the space transformation method, which enables wavelength manipulation by distributing an anisotropic medium that incorporates water depth and gravitational acceleration within the modulation space. First, annular wavelength modulators were designed using the space transformation method to reduce or amplify the wavelength of shallow water waves. The control method of wavelength scaling ratios was investigated. In addition to plane waves, the wavelength modulator was applied to manipulate the wavelength of cylindrical waves. Furthermore, the interactions between a vertical cylinder and modulated water waves were studied. Results indicate that the wavelength can be arbitrarily reduced or amplified by adjusting the dimensional parameters of the modulator. Additionally, the modulator is effective for plane waves and cylindrical waves. This wavelength modulator can enable the structure to achieve the desired scattering characteristics at the target wavelength.

The backscattering coefficient of aquatic particles (bbp(λ)) is one of the most important inherent optical properties in remote sensing. Due to the practical difficulties associated with measurements of the volume scattering function (VSF) over the whole backward hemisphere (90°–180°), bbp(λ) is estimated using either a single-angle approach, which employs the VSF at a fixed angle multiplied by a conversion factor χp(θ;λ), or a multi-angle approach, which uses the VSF at multiple angles with polynomial fitting. The angular variation in the VSF in the backward angles introduces uncertainties into bbp(λ) estimation. In this study, 178 VSF datasets from the global ocean were investigated. χp exhibited wavelength, regional, and angular variations. Although χp exhibited the lowest variability, at 120° (χp(120°;λ)), the single-angle approach exhibited a 12.71% mean absolute percent difference (MAPD) and a root mean squared error (RMSE) of approximately 4.02×10−3m−1. χp(140°;λ) exhibited larger variations at different wavelengths and in coastal regions. The three-angle approach exhibits wavelength independence and lower uncertainties, but the uncertainty of the polynomial fitting results at angles greater than 150° is relatively large, and the MAPD is still up to 10.92%. A better four-angle approach (100°, 120°, 140°, and 160°) was proposed, which could accurately determine bbp(λ) with the lowest MAPD (3.12%) and RMSE (0.86×10−3m−1). Notably, expanding to five angles provided minimal additional improvements, with the reduction in the MAPD being less than 1% compared to that under the four-angle approach. This study provides valuable insights into developing advanced optical sensors with better angular configurations for measuring bbp(λ).

Dissolved organic carbon (DOC) is the total carbon content of compounds dissolved in water, which is an important indicator of the marine carbon cycle and ecosystems and is of great significance to the carbon sink capacity of the oceans and the maintenance of marine ecosystems in equilibrium. The commonly used detection method for DOC in seawater is the laboratory chemical analysis method, but this detection method is complicated in operation and cumbersome in processing, and it does not have the ability to detect all-weather rapidly. To this end, this paper presents a method for detecting DOC in seawater based on ultraviolet absorption spectroscopy. A prediction model of DOC in seawater was constructed by partial least squares (PLS) and calibrated by multiple linear regression (MLR) methods, which completed the detection of DOC in 10 sets of actual unfiltered seawater samples. The results showed that using the constructed model to correct the spectral data containing impurities such as particles, significantly enhanced the correlation of measured dissolved organic carbon. Specifically, the linear correlation coefficient R2 improved from 0.8891 to 0.9838. Furthermore, the mean absolute error (MAE) decreased from 9.775

Nowadays, spaceborne LiDAR technology, particularly ICESat-2, has become a transformative tool in marine environmental research. Unlike traditional passive optical remote sensing methods, ICESat-2 offers detailed vertical structure mapping of oceanic optical properties. Despite the potential of ICESat-2 for observing the optical vertical structure, its application in the East China Sea with complex hydrological conditions and dynamic ecosystems remains limited. In this study, we introduce an innovative methodology for retrieving the vertical structure of subsurface optical properties in the East China Sea using ICESat-2 spaceborne LiDAR observations. After preprocessing ICESat-2 ATL03 data, we employed a 4 km × 1 m bin with a 0.15 m depth step for sliding accumulation, allowing us to capture LiDAR signals at various water depths. Following deconvolution, we proposed a method to calculate the vertical profiles of the diffuse attenuation coefficient and the particulate backscatter coefficient, thereby obtaining their vertical distributions. Our retrieval results show a high degree of consistency with MODIS products and BGC-Argo data, particularly in clearer open waters. The optical parameters in the East China Sea exhibit a distinct spatial pattern, with elevated values in the western and northern regions and lower values in the eastern and southern regions. This distribution is largely attributed to the proximity of the northern laser track segments to land and the influence of terrestrial runoff from the Yangtze River on the western side of the East China Sea. The influx of suspended particles and nutrients in this region significantly affects the magnitude of optical parameters, resulting in higher root mean square errors (RMSE) compared to the eastern waters. Moreover, our analysis reveals notable differences in the vertical distribution of the diffuse attenuation coefficient and the particulate backscatter coefficient, reflecting varying concentrations of optically active components across different water layers. These findings validate the efficacy of ICESat-2 for retrieving the vertical structure of subsurface ocean optical properties, providing a robust foundation for understanding the dynamic changes within the East China Sea ecosystem.