The surface water and ocean topography (SWOT) mission is currently operating in scientific orbit. The Ka-band radar interferometer (KaRIn) altimeter represents a significant departure from nadir altimeters, offering abundant observations that would greatly advance oceanographic research. However, the accuracy of altimetry data is crucial to the validity of research results and must be evaluated using crossover discrepancies to obtain prior estimations. Traditional methods are not fully suited to the wide-swath data and tend to be inefficient when processing massive amounts of data. In this study, we propose an improved latitude difference method for calculating crossover discrepancies. The reliability of this algorithm is verified using both along-track and across-track split data. The results indicate that the accuracy of crossover discrepancies for SWOT is comparable to those of conventional altimetry satellites, confirming the performance of the SWOT low-rate L2 KaRIn product. The standard deviation of crossover discrepancies between SWOT and other satellites in the South China Sea (SCS) is about 8 cm, around 6 cm in the Indian Ocean (southern) (IOS), and approximately 8 cm in the Gulf of Mexico (GOM), further proving the accuracy of the SWOT data. Analyzing the discrepancies beyond 50 km offshore, the results demonstrate that the KaRIn altimeter is influenced by the coastline, while its performance improves in the open ocean. By leveraging the vectorization algorithm, the efficiency has been improved significantly. The computation speed of crossover discrepancies using the improved latitude difference method is around 0.006 s per point, faster than the traditional method, demonstrating that the algorithm is efficient.

Marine ranching plays a vital role in sustainable fishery resource utilization and marine ecosystem protection. Accurate water quality monitoring through remote sensing is essential in these areas; however, aquaculture facilities such as floating buoys introduce additional reflected signals that can distort remote sensing reflectance (Rrs(λ)), leading to significant errors in water quality parameter retrievals. Despite this challenge, previous studies have neither systematically quantified this interference nor developed effective correction methods. This study proposes a novel correction method to mitigate aquaculture-induced distortions inRrs(λ), enhancing the accuracy of remote sensing-based water quality assessments. Using the mussel aquaculture ranching area off Gouqi Island, China, as a case study, we systematically analyze the spectral influence of aquaculture facilities onRrs(λ) derived from Landsat observations. Comparative spectral analysis between affected and unaffected areas reveals that the high reflectance characteristics of aquaculture facilities cause abnormally elevatedRrs(λ) values, particularly in the shortwave infrared bands. To address this issue, we introduce an aquaculture facility influence factor and develop a pixel-based dynamic correction approach that adjusts for varying degrees of aquaculture-induced distortions across different pixels. Validations using field-measuredRrs(λ) demonstrates that the mean absolute percentage errors at wavelengths of 443, 483, 561, 655, 865, and 1609 nm decreased significantly from 17.6%, 19.8%, 14.8%, 26.8%, 50.9%, and 180.6% before correction to 9.5%, 11.0%,9.2%, 10.4%, 7.7%, and 20.4%, respectively. The effectiveness of the correction method is further supported by improvements in the retrieval of water transparency (Zsd). Sensitivity analysis further reveals that uncorrectedZsdretrieval errors increase exponentially with aquaculture facility coverage, exceeding 60% when coverage reaches 10%, underscoring the necessity of correctingRrs(λ) distortions caused by aquaculture facilities. Overall, the proposed correction method provides a robust and adaptable framework for improving satellite-based water quality monitoring in complex aquaculture regions, with potential applicability across diverse marine ranching environments and integration with various satellite sensors.

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) provides abundant ocean satellite altimetry data. Photon point clouds data denoised by the official ATL03 algorithm exhibit insufficient continuity, and classical gravity anomaly inversion algorithms suffer from high computational complexity. To address these issues, we propose a two-step denoising method for ATL03 data to obtain instantaneous sea surface heights (SSHs): photon point clouds data are denoised using the adaptive OPTICS algorithm, followed by secondary denoising using the Linear-Interquartile algorithm. The resulting SSHs demonstrate superior continuity and larger data volume than those of ATL12 data. This paper proposes the Attention-GraphSAGE algorithm—a graph neural network approach based on neighbor node sampling and self-attention-weighted feature aggregation. An Adam optimizer with L2 regularization is employed for iterative training to achieve nonlinear fitting. The architecture incorporates two-layer neighbor node sampling and aggregation, with residual connections between layers to prevent gradient explosion and preserve original data features. During model training, each input contains 81×81×4 feature values. Nodes consist of shipborne measurement points and surrounding grid points; edges represent connection relationships between shipborne points and first-layer neighbor nodes (grid points), as well as connections between grid points and second-layer nodes (grid points). Edge weights are determined by calculating the correlation coefficient of geoid height between each neighbor node and the shipborne measurement point (using self-attention mechanism). The output corresponds to the difference between shipborne gravity anomaly data and SIO V32.1 gravity anomaly data. The gravity anomaly model inverted utilizing denoised photon with the Attention-GraphSAGE algorithm (AGP-GRA model) demonstrated a correlation coefficient of 0.99 and a standard deviation of 3.36 mGal with shipborne gravity anomaly data. Compared to the model inverted directly utilizing ATL12 data with the same algorithm (AGA-GRA model), this represents a standard deviation reduction of 0.09 mGal. Experiments confirm the algorithm’s effectiveness for gravity anomaly inversion demonstrating with favorable model performance.

Obtaining accurate ocean subsurface temperature (ST) and salinity (SS) data is vital for studying oceanic processes. Given the scarcity of in situ observational data and the abundance of satellite remote sensing data, this study proposes an improved transformer-based temporal and spatial fusion network (TTSFNet) to invert ST and SS. This method utilizes sea surface height anomaly (SSHA), sea surface temperature anomaly (SSTA), sea surface salinity anomaly (SSSA), and sea surface wind speed anomaly (SSWA), combined with vertical temperature and salinity features, to invert the ocean ST and SS anomalies (STAs/SSAs). To fully utilize vertical distribution characteristics, this study introduces the derived variables, the difference in SSTA (DSSTAs) and the difference in SSSA (DSSSAs), to enrich the data dimension. The temporal and spatial feature enhancement blocks (TFEBs/SFEBs) are designed in the TTSFNet to enhance spatiotemporal feature representation. Combining convolution operations within the transformer framework to process 2-D data allows for more effective extraction of spatial features. Inversion experiments conducted at water depth ranging from 5 to 2000 m in the North Pacific Ocean demonstrate that integrating both temporal and spatial features is more effective than considering them individually. The average$R^{2}$and root mean square error (RMSE) for the inversion results of STA and SSA at all depths in the North Pacific Ocean in 2021 are 0.705 and 0.510 for STA, and 0.375 and 0.052 for SSA, respectively. Compared to existing deep learning models, TTSFNet demonstrates superior performance. Overall, the model excels in spatiotemporal feature learning and accurately inverts the ST and SS structure.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult, and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this paper, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves a SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

The radar altimeter onboard China's HY-2B satellite was launched in October 2018. In this paper, we processed the HY-2B L1 altimetry data from October 2020 to April 2021. The radar freeboard was preliminarily estimated from the HY-2B altimetry data over the Arctic sea ice. We validated the HY-2B radar freeboard estimates using the radar freeboard products from the Alfred Wegener Institute (AWI). The overall difference between the HY-2B radar freeboard estimates and the AWI data is$0.088\pm 0.057\ \mathrm{m}$.

The radar altimeter onboard China's HY-2B satellite was launched in October 2018. In this paper, the precision of the HY-2B elevation measurements over the Antarctica ice sheet (AIS) and the Greenland ice sheet (GrIS) is assessed through crossover analysis. The TFMRA (threshold first maximum re-tracker) algorithms is applied to retrack the waveforms of HY-2B altimeter. The precision of the HY-2B altimeter elevations is found to vary as a function of the threshold of the surface slope from$\sim 48 \text{ cm}$to$\sim 51\text{ cm}$over the AIS, and from$\sim 23\text{ cm}$to$\sim 34 \text{ cm}$over the GrIS. When compared with results obtained from time-coincident SARAL altimeter, the standard deviation of the crossover differences between the SARAL and HY-2B altimeters varies from$\sim 68 \text{ cm}$to$\sim 101 \text{ cm}$over the AIS, and from$\sim 56\text{ cm}$to$\sim 102\text{ cm}$over the GrIS.

The azimuth cutoff wavelength of SAR is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on H-V linear polarization bases (HH, HV/VH, and VV) without considering the effects of other polarization bases (e.g., linear rotated, circular, and elliptical polarization). Benefiting from the quad-polarization advantage of GaoFen-3 SAR wave mode data and the support of polarization basis transformation theory, this study used 4,648 SAR data to study the correlation between cutoff wavelength and wind and wave parameters (e.g., significant wave height, and wind speed) under different polarization bases, and analyzed the variation of correlation coefficient caused by polarization basis change. Finally, the results were applied to evaluating the performance of wind and wave parameters retrieval. The results of the study show that the azimuth cutoff is strongly dependent on the polarization state of electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has higher correlation with wind and wave than that under H-V linear, circular, and linear rotated polarization bases. Using the azimuth cutoff wavelength of the elliptical polarization bases can significantly improve the retrieval accuracy of wind and wave parameters. This study shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties. The result implies that polarization basis is an important factor that must be considered in future ocean SAR studies.

In this study, an Attention U-net network was proposed to reconstruct the subsurface temperature (ST) field with high temporal and spatial resolution in the South China Sea (SCS) from sea surface parameters observed by satellites. In addition to sea surface temperature, sea level anomaly and sea surface wind field, the wind stress curl, which influences three-dimensional structure of temperature through the induced Ekman pumping and transport, was also input into the model. The 5-day average vertical temperature profiles with spatial resolution of 0.5° from Simple Ocean Data Assimilation (SODA) reanalysis were used for training and evaluating the network. The results show that the Attention U-net model performs quite well in ST reconstruction in the upper 100 m layers of the SCS. The additional input of wind stress curl helps to improve the model accuracy. The average root mean square error (RMSE)/bias of ST decreases from 1.08°C/-0.21°C to 1.01°C/-0.05°C. Particularly, the RMSE near the thermocline is reduced significantly by up to 10.9%. The estimation error of the Attention U-net model is much smaller than that of some linear and tree models in the SCS, especially in shallow waters and regions with complex dynamic processes. The case study also shows that our model is capable of capturing the evolution of mesoscale processes in the SCS. The combination of satellite observations with high-precision ST reconstruction model will help us comprehensively understand the fine structure and variation of temperature and circulation in the marginal seas and open oceans.

This paper assesses the sea surface height (SSH), significant wave height (SWH) and wind speed (U) measurements derived from Haiyang-2B (HY-2B) altimeter products from April 2019 to September 2019. Crossover analysis is used to assess to SSH measurements. The mean standard deviations of the SSH crossover differences for HY-2B is 5.09 cm, which is lower than Jason-2 (5.31 cm) and Jason-3 (5.27 cm). Compared with the National Data Buoy Center (NDBC) observations, the HY-2B SWH measurements show a root-mean-square error (RMSE) of 0.205 m with a positive bias of 0.150 m. The HY-2B wind speed measurements show a RMSE of 1.139 m/s with a negative bias of 0.563 m/s. The SSH, SWH and wind speed measurements from HY-2B products show high accuracy, but should be further calibrated.

Freeman-Durden decomposition is a frequently used technique to analyze the scattering characteristics of multilook Polarimetric Synthetic Aperture Radar (POLSAR) data. When it is applied to real POLSAR data, two problems emerge, which are the volume scattering overestimation and negative powers. Many researchers think these two problems are caused by the insufficient decomposition algorithm, and several improvements are proposed. However, the improved decomposition algorithms become more and more complicated, and some new problems such as the decomposed component is not model-based also emerge. In this article, we try to solve the two problems through another way. We think they are caused by the dogmatic input rather than the insufficient decomposition algorithm. Freeman-Durden decomposition explicitly assumes reflection symmetry. Its input is a direct truncation of the measured coherency matrix. The truncation can be regarded as a Reflection Symmetry Approximation (RSA) of the measured coherency matrix. We firstly show some reasons why we think the truncation is not a good RSA. Then a new RSA is proposed based on the sum of three reflection symmetry components derived from the measured coherency matrix. Experimental results with several real POLSAR images show that, if the new RSA is used as the input of Freeman-Durden decomposition, the abovementioned two problems no longer exist.

The HY-2B satellite is the second dynamic environment satellite in China. It was successfully launched on October 24th 2018 with a sun-synchronous orbit at an altitude of~970km. Repeat cycles of 14 days are planned for the first two years with oceanographic purpose and 168 days geodetic cycles will follow for the third year of the mission. The satellite is equipped with a Ku/C bands altimeter and the orbit is determined thanks to SLR, GPS and DORIS systems.

Detecting ocean dynamical environment has a very important significance for Ocean monitoring and forecast, and satellite remote sensing is an important approach. There is a special satellite series for detecting Ocean dynamical environment in Chinese satellite system, and some of them has begun to play an important role in this field. In the future, Chinese Ocean Dynamical Environment Satellites will continue to develop, various kinds of new Satellites will be launched, and the capability of detecting ocean dynamical environment will be better.

HY-2 satellite was successfully launched on 16 August, 2011. It carried three main microwave instruments into space for operationally observing dynamic ocean environment parameters on a global scale. HY-2 satellite altimeter provides sea surface height, significant wave height, sea surface wind speed. Current status of HY-2 satellite altimeter is put forward in this study. By comparison with the other satellite data, NDBC data and other data, the accuracy of the HY-2's data products is evaluated in this work.

The Bohai sea is a semi-enclosed sea located in the northeast of China. Safety of ship navigation and exploration platforms are important issues. Reliable near real time ice information is necessary and the ice thickness distribution is still a challenge. Here we present an ice thickness estimation method combining numerical sea ice model and SAR data. A high resolution thermodynamic snow and ice model (HIGHTSI) is applied to calculate the thermodynamic ice growth and used as the ice thickness background. SAR images are used to express the local ice statistics and to redistribute the modeled ice thickness. The results are evaluated by comparing with in-situ observations from oil platforms and ice forecast results from National Marine Environmental Forecasting Center (NMEFC).