Search Results (115)

Accurate sea surface temperature (SST) forecasting underpins operational oceanography and climate surveillance, yet it remains constrained by the inherent multiscale spatiotemporal variability. Existing deep learning methods often struggle to identify the physical hierarchies of SST fields, typically treating them as homogeneous inputs and thus failing to decouple large-scale coherent structures from transient features effectively. To bridge this gap, this study introduces MS-SSTNet, a scale-aware framework designed for spatiotemporal SST forecasting that leverages iterative multiscale decomposition. By iteratively distilling SST fields into hierarchical spatial modes and their principal components (PCs), the architecture facilitates a rigorous scale-decoupling representation of ocean dynamics. A dual-window temporal module is then integrated to characterize the coupling between long-term persistent trends and short-term stochastic fluctuations. Evaluations using satellite-derived SST products over the South China Sea (SCS) demonstrate that MS-SSTNet achieves robust 10th day forecast skill, yielding an overall spatiotemporal average MAE of 0.3031 °C, an average RMSE of 0.4062 °C, and an average ACC of 0.7973 across the entire 1–10 day forecast horizon. Ablation studies further underscore the indispensability of multiscale decomposition and dual-window integration in enhancing forecast fidelity across diverse spatiotemporal scales. Full article

Oceanographic buoys are essential platforms for marine observation, supporting real-time monitoring, climate studies, operational oceanography, coastal management, and offshore activities. This review maps the scientific literature on oceanographic buoys and identifies the main research sectors that structure the field. A Scopus search using TITLE-ABS-KEY((oceanographic OR metocean OR “ocean observing” OR “ocean observation”) AND (buoy OR buoys)) retrieved 1876 documents published between 1938 and 2026. The records were processed using a network-based sector detection and visualization workflow. Five thematic sectors were identified: platform engineering, sensors, moorings, and autonomous systems; marine wind, waves, metocean, and offshore energy applications; physical oceanography, sea-surface temperature (SST), currents, and environmental variability; coastal and ocean observing networks and operational services; and drifting buoys, floats, and global ocean datasets. The results show that buoy research has evolved from isolated measurement systems to integrated observing infrastructures that combine in situ sensing, satellite validation, autonomous operation, telemetry, and decision-support services. By linking bibliometric structure and thematic interpretation, this review provides a sector-based map showing where scientific observation, engineering design, operational services, and autonomous technologies converge. Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

This paper discusses the assimilation of significant wave height (Hs) observations from the China France Oceanography SATellite (CFOSAT) into the Global Deterministic Wave Prediction System developed by Environment and Climate Change Canada. We focus on the quantification of background errors in an effort to address the conventional, simplified, homogeneous assumptions made in previous studies using Optimal Interpolation (OI) to generate Hs analysis. A map of Best Correlation Length, L, is generated to count for the inhomogeneity in the wave field. This map was calculated from pairs of Hs forecasts of two grid points shifted in space and time from which a look-up table is derived and used to infer the spatial extent of correlations within the wave field. The wave spectra are then updated from Hs analysis using a frequency shift scheme. Results reveal significant spatial variance in the distribution of L, with notably high values located in the eastern tropical Pacific Ocean, a pattern that is expected due to the persistent swells dominating in this region. Experiments are conducted with spatially varying correlation lengths and a set correlation length of eight grid points in the analysis step. Forecasts from these analyses are validated independently with the Global Telecommunications System buoys and the Copernicus Marine Environment Monitoring Service (CMEMS) altimetry wave height observations. It is found that the proposed statistical method generally outperforms the conventional method with lower standard deviation and bias for both Hs and peak period forecasts. The conventional method has more drastic corrections on Hs forecasts, but such corrections are not robust, particularly in regions with relatively short spatial correlation length scales. Based on the analysis of the CMEMS comparison, the globally varying correlation length produces a positive increment of the Hs forecast, which is globally associated with forecast error reduction lasting up to 24 h into the forecast. Full article

Sea surface height (SSH) derived from satellite altimetry is essential for oceanographic research and marine monitoring. Although artificial intelligence (AI) models show considerable potential in forecasting, their application in oceanography remains constrained by several limitations. To address these challenges, we propose a set of physics-informed methods to improve SSH prediction based on neural networks in the South China Sea (SCS). The key strategies include: (1) incorporating land mask information to mitigate artifacts induced by the presence of land in marine data; (2) introducing a geostrophic constraint into the loss function; and applying latitude-dependent weighting to this constraint to account for the breakdown of geostrophic balance near the equator. On the test dataset, the physics-informed SimVPv2 (Phys-SV) model achieves an RMSE of 0.0173 m, a 13% improvement over the baseline SimVPv2 (Base-SV). The PredRNNv2 (PR) model also benefits significantly from the inclusion of land mask input, with RMSE reduced by 12% (from 0.0280 m to 0.0246 m). To the best of our knowledge, this study is the first to identify the artifact issue in AI models caused by land points in ocean data and to reveal the limitations of directly concatenating heterogeneous oceanic variables as model inputs. Full article

Three-dimensional (3D) ocean physical fields are essential for understanding ocean dynamics, but reconstructing them solely from sea-surface remote sensing remains challenging. We present 3DV-Unet, an end-to-end deep learning framework that reconstructs eddy-resolving three-dimensional essential ocean variables (temperature, salinity, and currents) from multi-source satellite data. The model employs a 3D Vision Transformer bottleneck to capture cross-depth and cross-variable dependencies, ensuring physically consistent reconstruction. Trained on 2011–2019 reanalysis and satellite data, 3DV-Unet achieves RMSEs of ~0.30 °C for temperature, 0.11 psu for salinity, and 0.05 m/s for currents, with all R² values above 0.93. Error analyses further indicate higher reconstruction errors in dynamically complex regions such as the Kuroshio Extension, while spectral analysis indicates good agreement at 100 km+ but systematic deviation in the 20–100 km band. Independent validation against 6113 Argo profiles confirms its ability to reproduce realistic vertical thermohaline structures. Moreover, the reconstructed 3D fields capture mesoscale eddy structures and their life cycle, offering a valuable basis for investigating ocean circulation, energy transport, and regional variability. These results demonstrate the potential of end-to-end volumetric deep learning for advancing high-resolution 3D ocean reconstruction and supporting physical oceanography and climate studies. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

Sea Surface Salinity is a crucial climatic variable due to its twofold role as both a passive and an active tracer of oceanic processes. Despite its relevance, however, it could not be measured from space, mainly because of technological limitations, until 2009. Since then, the generation and assessment of satellite salinity has become a game-changer in physical and biogeochemical oceanography, as well as in climate science. Three satellite sensors with salinity-measuring capabilities (SMOS-Soil Moisture and Ocean Salinity, Aquarius, and SMAP-Soil Moisture Active Passive) have been launched in the previous decade, each characterized by specific measurement concepts and features and ad hoc validation approaches. The increasing usage of spaceborne salinity products has produced a variety of results and applications, which are here summarized under three specific domains: climate, scientific, and operational. Finally, short-to-mid-term perspectives, indicating both the expected improvements in terms of algorithms and also looking at novel mission concepts (that will provide continuation of these measurements in the decade to come) have been described. Full article

Ocean surface radial current velocities can be derived from synthetic aperture radar (SAR) Doppler shift observations using the Doppler centroid technique and a recently developed Doppler velocity model. However, comprehensive evaluations of the accuracy and reliability of these retrievals remain limited. To address this gap, we analyzed 6341 Sentinel-1 SAR scenes acquired over the South China Sea (SCS) between December 2017 and October 2023, in conjunction with drifting buoy observations, to systematically validate the retrieved radial current velocities. A linear fitting method and the dual co-polarization Doppler velocity (DPDop) model were applied to correct for the influence of non-geophysical factors and sea state effects. The validation against the drifter data yielded a bias of 0.01 m/s, a root mean square error (RMSE) of 0.18 m/s, and a mean absolute error (MAE) of 0.16 m/s. Further comparisons with the Surface and Merged Ocean Currents (SMOC) dataset revealed bias, RMSE, and MAE values of 0.07 m/s, 0.14 m/s, and 0.12 m/s in the Beibu Gulf, and −0.06 m/s, 0.23 m/s, and 0.19 m/s in the Kuroshio intrusion area. These results demonstrate that SAR Doppler measurements have a strong potential to complement existing ocean observations in the SCS by providing high-resolution (1 km) ocean surface current maps. Full article

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m⁻² under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography. Full article

Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the existing studies based on deep learning have constructed mappings from low-resolution inputs to high-resolution downscaled estimates. However, these methods have failed to capture the relationships between multiple variables as revealed by physical processes. Therefore, this paper proposes a spatial downscaling approach for satellite sea surface wind that employs soft-sharing multi-task learning. Sea surface temperature and water vapor are included as auxiliary variables for SSW, considering the close correlation revealed by physical principles and data availability. The spatial downscaling of auxiliary variables is designed as an auxiliary task and integrated into a multi-task learning network with generative adversarial network and dual regression structures. The proposed multi-task downscaling network achieves flexible parameter sharing and information exchange between tasks through a soft-sharing mechanism and bridge modules. Comprehensive experiments were conducted with WindSat SSW products at 0.25° from Remote Sensing Systems. The experimental results validate the outstanding downscaling capability of the proposed methodology with respect to precision in comparison with buoy measurements and reconstruction quality. Full article

The ScanSAR mode image obtained by the Gaofen-3 (GF-3) satellite has an imaging width of up to 130–500 km, which is of great significance in monitoring oceanography, meteorology, water conservancy, and transportation. To address the issues of subswath misalignment and the inability to correct in the processing of GF-3 ScanSAR images in coastal areas using software such as PIE, ENVI, and SNAP, a method for mosaicking and correcting GF-3 ScanSAR images with subswaths that overlap within specified range constraints is proposed. This method involves correlating the coefficients of each subswath thumbnail image in order to determine the extent of the overlap range. Given that the matching points are constrained to the overlap between subswaths, the normalized cross-correlation (NCC) matching algorithm is utilized to calculate the matching points between subswaths. Subsequently, the random sampling consistency (RANSAC) algorithm is employed to eliminate the mismatching points. Subsequently, the subswaths should be mosaicked together with the stitching translation of subswaths, based on the coordinates of the matching points. The image brightness correction coefficient is calculated based on the average grayscale value of pixels in the overlapping region. This is performed in order to correct the grayscale values of adjacent subswaths and thereby reducing the brightness difference at the junction of subswaths. The entire ScanSAR slant range image is produced. By employing the Range–Doppler model for indirect orthorectification, corrected images with geographic information are generated. The experiment utilized three coastal GF-3 ScanSAR images for mosaicking and correction, and the results were contrasted with those attained through PIE software V7.0 processing. This was conducted to substantiate the efficacy and precision of the methodology for mosaicking and correcting coastal GF-3 ScanSAR images. Full article

Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the deflection of vertical method and sea surface height method, respectively. The fusion method based on the offshore distance used in the EGM2008 model is just model stitching, which cannot realize the true fusion of the two types of marine gravity field models. In the paper, a new fusion method based on water depth segmentation is proposed, which established the Precision–Depth relationship of each model in each water depth segment in the investigated area, then constructed the FUSION model by weighted fusion based on the precision predicted from the Precision–Depth relationship at each grid in the whole region. The application in the South China Sea shows that the FUSION model built by the new fusion method has better accuracy than SIO28 and DTU17, especially in shallow water and offshore areas. Within 20 km offshore, the RMS of the FUSION model is 5.10 mGal, which is 8% and 4% better than original models, respectively. Within 100 m of shallow water, the accuracy of the FUSION model is 4.01 mGal, which is 14% and 12% higher than the original models, respectively. A further analysis shows that the fusion model is in better agreement with the seabed topography than original models. The new fusion method can blend the effective information of original models to provide a higher-precision marine gravity field. Full article

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. Full article

The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions. Full article