Sea-surface wind fields are critical parameters in marine environments, influencing ocean circulation, meteorology, and climate dynamics. To assess the accuracy of satellite-derived ocean wind products and characterize their error distribution, this study validates sea-surface wind field retrievals using a combination of satellite remote sensing and ocean buoy measurements. Wind field estimates from the Advanced Scatterometer (ASCAT) aboard the European Organisation for the Exploitation of Meteorological Satellites’ MetOp series were compared against buoy observations from four buoy database including the U.S. National Data Buoy Center, et al. over the 2013—2022 period. Following data preprocessing and spatiotemporal collocation, statistical metrics—including mean bias, root-mean-square error, and correlation coefficients—were employed to evaluate ASCAT wind field accuracy. Results indicate strong agreement between ASCAT-derived and buoy-measured wind fields, with correlation coefficients of 0.928 for wind speed and 0.867 for wind direction. The standard deviation of wind speed is 0.889 m/s, while that of wind direction is 22.168°. Among buoy networks, NDBC sites exhibited the most stable wind speed and direction deviations. This validation study enhances the reliability of satellite-derived wind fields, contributing to improved weather forecasting, climate research, ocean engineering, and disaster warning systems. Additionally, the findings support the continuous refinement of satellite payloads and retrieval algorithms.

Oceanic carbon flux constitutes a critical component of the global carbon cycle and fundamentally informs climate change modeling and prediction. The advent of light detection and ranging(LiDAR) remote sensing has gradually revolutionized oceanic carbon flux measurements by providing high spatiotemporal resolutions, precision, and real-time monitoring capabilities. This review evaluates recent advances in LiDAR-based monitoring of diurnal-nocturnal oceanic carbon flux dynamics. We examine the fundamental principles, methodological approaches, and technical challenges associated with LiDAR applications in carbon flux quantification across the air-sea interface. Additionally, we identify knowledge gaps and propose future research directions to enhance the efficacy of LiDAR technology in characterizing temporal variability in oceanic carbon sequestration.

Accurate assessment of the scale and distribution of offshore marine aquaculture is critical for effective management, spatial planning, and ecological protection. This study employed high-resolution Sentinel-2A/2B satellite imagery, a U-Net deep learning model for automatic feature extraction, and human-computer interactive correction to map the spatial extent of raft-based kelp farming in Heiniwan Bay from 2016 to 2024. The analysis revealed a three-phase development trajectory in the aquaculture area over the nine-year period. Spatial distribution exhibited a stable “north-south agglomeration with central sparsity” pattern. The observed spatiotemporal dynamics reflect the combined influence of technological advancements, policy interventions, and natural environmental conditions. These findings offer a robust scientific basis for optimizing aquaculture zoning, adaptive management strategies, and ecological governance in coastal regions with comparable aquaculture practices and environmental settings.

Using Level-2 low-rate expert-level data obtained from the Surface Water and Ocean Topography (SWOT) satellites, this study calculates the positions of crossover points using a gridding method, quantitatively analyzes self-crossover discrepancies, and investigates the impacts of various factors—the time difference between the crossover points, crossover distance, distance from the coast, and latitude—on these discrepancies.Results show that in most regions, discrepancies fall within the range of -0.2 m to 0.2 m, indicating good data consistency, although notable discrepancies are observed in specific regions. Further analysis reveals that the distance between the crossover points has a minimal impact on discrepancies; discrepancies near the coastline are relatively dispersed, while those observed in regions farther from the coastline tend to decrease and stabilize. In high-latitude regions, discrepancies decrease. The time difference shows a certain degree of dispersion in its influence on discrepancies. In a certain range (within 2 km), the distance between the crossover points has a minimal impact on discrepancies. The findings of this study provide a scientific basis for improving the accuracy and reliability of altimetry data obtained from SWOT satellites.

Water quality monitoring and assessment are crucial for sustainable use of water resources and the preservation of aquatic ecosystems. Remote sensing technology, with its unique capability for long-distance detection, offers rapid, efficient, wide-coverage, and high-precision monitoring, and it is particularly suitable for large-scale, dynamic water environment surveillance. In recent years, China has made remarkable progress in the field of remote sensing satellite technology. The successful deployment and continuous high-quality data output of the domestic high-resolution satellite series have brought new opportunities for realizing advanced water quality monitoring. This paper systematically reviews and summarizes the current applications of data obtained from the Gaofen satellite series for water quality monitoring, focusing on the remote-sensing inversion of key water-quality parameters. Furthermore, it outlines future research directions, including the integration of multi-source data, optimization of algorithms and models, and the development of theoretical models. This study provides detailed references for the future development and research on water quality monitoring using domestic satellites.

Suspended sediment mass concentration (ρ_SSC) is a key indicator of estuarine water quality, influencing water transparency, turbidity, nearshore ecosystems, and shoreline stability. In this study, ρ_SSC dynamics in the Yellow River Estuary and adjacent sea areas were analyzed from 1984 to 2024 using satellite remote sensing data processed on the Google Earth Engine platform. We examined the spatiotemporal distribution patterns, long-term trends, and primary drivers of ρ_SSC changes. Over the 41-year period, ρ_SSC exhibited a general declining trend, with low-ρ_SSC waters increasingly dominant. High Suspended sediment mass concentration regions became more localized near the coast, primarily in southern Bohai Bay and the southwestern coast of Laizhou Bay, forming a banded distribution. Theil-Sen Median slope estimation and Mann-Kendall trend analysis revealed significant ρ_SSC increases in the Qingba waterway and artificial distributary channels, whereas significant decreases were observed in the Qingshui Ditch area. Bohai Bay and Laizhou Bay showed a slight upward trend overall. Human interventions, particularly river course diversions, significantly influenced ρ_SSC patterns: historical high Suspended sediment mass concentration estuarine zones contracted, whereas new high Suspended sediment mass concentration zones expanded seaward following each diversion.

Underwater three-dimensional (3D) imaging light detection and ranging (LiDAR) systems have the potential for accurately detecting underwater targets and mapping the seabed terrain, thus facilitating the development and utilization of marine resources. However, most existing underwater 3D imaging LiDAR systems suffer from large size and high power consumption, making them unsuitable for the operational requirements of underwater tasks. To overcome these issues, this study proposes a compact solution based on photon-counting technology that integrates single-point ranging with two-dimensional scanning to achieve 3D imaging. A compact underwater photon-counting 3D imaging LiDAR system was developed by optimizing optical and mechanical design, resulting in a device with a diameter of 165 mm and a length of 340 mm, considerably improving portability and underwater adaptability. A dual-axis synchronous scanning control method was implemented based on FPGA to achieve a scanning accuracy at the nanosecond level, ensuring precise alignment between the emitted pulse and measured target point. Laboratory water tank experiments revealed that the system has a detection capability exceeding 3.1 attenuation lengths. Furthermore, this system was used for underwater 3D imaging of a thruster model that validates its centimeter-level ranging accuracy. Owing to its strong compatibility, this system can be integrated into various underwater mobile platforms and holds strong potential for applications such as seabed topographic mapping, underwater cultural heritage detection, and underwater target identification.