Search Results (48)

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

The Tyrrhenian Sea plays a key role in Mediterranean circulation. However, the seasonal evolution and coastal circulation patterns of its surface circulation remain only partially resolved by previous observations, particularly at mesoscale and submesoscale. In this study, we investigate the seasonal and coastal variability of Tyrrhenian Sea surface circulation by combining high-resolution wide-swath altimetry from the Surface Water and Ocean Topography (SWOT) mission with conventional multi-mission altimetry, satellite-derived Sea Surface Temperature (SST), and coastal tide gauge observations. Compared to conventional gridded altimetry, SWOT-enhanced products exhibit higher Absolute Dynamic Topography (ADT) gradients, with mean seasonal increases of 4–6% in winter-spring and up to 12–15% in summer-autumn. Surface velocity fields also exhibit generally higher spatial variability (3–10% in most seasons) and stronger mean velocities, with mean seasonal increases of up to 14% relative to conventional gridded altimetry. These differences result in a finer spatial depiction of frontal systems, coastal jets, and mesoscale circulation features. These patterns are consistent with independent tide gauge records and the spatial agreement between altimetry-derived structures and SST frontal gradients in representative seasonal case studies. While confirming the well-known seasonal cyclonic circulation, which is stronger in winter-spring and weaker in summer, our results indicate that SWOT-enhanced altimetry offers a more detailed spatial representation of fine-scale coastal and mesoscale circulation features than conventional gridded altimetry. Overall, these findings highlight the potential of SWOT-enhanced altimetry to improve the spatial characterization of Mediterranean upper-ocean circulation and its fine-scale variability, providing a valuable framework for future studies of coastal dynamics and mesoscale processes. Full article

Accurate characterization of flood dynamics in large river–lake systems remains challenging due to the difficulty of simultaneously capturing inundation extent and water surface elevation (WSE) variations under rapidly changing hydrological conditions. This study develops an integrated Synthetic Aperture Radar (SAR) and wide-swath interferometric altimetry framework to reconstruct the spatiotemporal evolution and storage dynamics of the 2024 flood event in the East Dongting Lake system, China. Sentinel-1 SAR imagery is utilized to derive high-resolution inundation extent, while the Surface Water and Ocean Topography (SWOT) mission, equipped with the Ka-band Radar Interferometer (KaRIn), provides two-dimensional WSE observations. To improve SAR-based flood extraction in heterogeneous floodplain environments, an Adaptive Spatially-Constrained Fuzzy C-Means (AS-FCM) algorithm is proposed by incorporating adaptive spatial regularization and structure-aware neighborhood weighting. Quantitative evaluation demonstrates that the proposed method achieves the highest performance among the evaluated conventional approaches, with an Overall Accuracy of 93.6%, an Intersection over Union of 0.89, and a Kappa coefficient of 0.87. The multi-temporal inundation sequence reveals a distinct flood evolution pattern characterized by rapid expansion during the rising stage and gradual recession during the post-peak period. SWOT-derived WSE observations exhibit strong agreement with synchronous in situ measurements after bias adjustment, with a correlation coefficient of 0.988. By integrating SAR-derived inundation extent with temporally matched water-level observations constrained by bias-adjusted SWOT and in situ gauge data, an empirical WSE–area relationship ($R^2=0.937$) is established to reconstruct daily flood dynamics and estimate cumulative water storage variation. The results indicate that the East Dongting Lake floodplain played an important buffering role during the 2024 flood event, with cumulative storage variation reaching approximately $10.7{\mathrm{km}}^3$ during the peak stage. Overall, the proposed framework demonstrates strong potential for flood monitoring and hydrological storage assessment in complex river–lake systems. Full article

High-quality water surface elevation (WSE) measurements are critical in hydrological applications, yet no systematic evaluation of the Surface Water and Ocean Topography (SWOT) mission exists for Brazil’s diverse lake systems, where satellite observations are essential given limited in situ monitoring. We evaluated WSE from SWOT over 132 Brazilian lakes, comparing LakeSP, Raster_250m, and Raster_100m products against field measurements over a 20-month period. The 68th percentile errors were under 29 cm for the full dataset, below 12 cm for Flag = 0, and below 21 cm for Flag = 1, indicating good agreement but also the presence of outliers and the need for data screening. A Random Forest analysis identified quality flags, lake geometry, and cross-track distance as key drivers of WSE precision. Flag = 0 is overly restrictive, retaining only 22% of observations, while Flag = 1 contains anomalous data. The SWOT Quality-Range Threshold for Lakes (SQRTL) filter combines Flag = 0 with cross-track constrained Flag = 1 observations. SQRTL more than triples data availability relative to Flag = 0, maintaining comparable precision (68th percentile below 16 cm) and reducing median revisit from 88–123 days to 16–18 days for raster products and from 25 to 14 days for LakeSP. These results provide the first large-scale SWOT WSE evaluation over Brazilian lakes and a transferable filtering framework applicable wherever SWOT and field observations overlap, with potential to extend monitoring to over 100,000 water bodies in the SWOT Prior Lake Database. Full article

The Surface Water and Ocean Topography (SWOT) mission carries a Ka-band interferometric radar altimeter (KaRIn), which enables high-resolution wide-swath measurements of sea surface height, providing new opportunities for deriving high-precision marine gravity fields. The discretization method used by the Scripps Institution of Oceanography (SIO) is one of the simplest methods for deriving deflections of the vertical (DOV), as it avoids parameter estimation and complex mathematical procedures. However, this method only uses adjacent observations for first-order differentiation and ignores diagonal directions, resulting in relatively low data utilization for SWOT/KaRIn data. The optimized discretization method is proposed to take advantage of the two-dimensional characteristics of KaRIn data. Multi-directional data is introduced to estimate the DOV (SWOT_DOV), and the numerical differentiation strategy is extended to higher orders. These significantly improve the solution quality. The standard deviation (STD) of the differences between SWOT_DOV and north_32.1 is 1.60 μrad, and that with east_32.1 is 2.02 μrad. Gravity anomalies are further derived using the inverse Vening-Meinesz formula. Validation using NCEI shipborne gravity data indicates an STD of 3.85 mGal. Further analyses considering seafloor topography gradient, depth, and offshore distance demonstrate that SWOT/KaRIn data have a stable capability to restore high-precision marine gravity field features. Full article

Mesoscale eddies play a key role in oceanic transport, yet their characterization in marginal seas like the Gulf of California remains challenging due to complex coastlines and bathymetry that hinder conventional detection methods. This study addresses this gap by presenting a robust hybrid framework—integrating dynamical (Okubo–Weiss), velocity geometry (Nencioli), and closed-contour (Chelton) criteria—applied to the high-resolution (${0.01}^{\circ }$) Neural Ocean Surface Topography (NeurOST) altimetry product (2010–2024). Temporal continuity is ensured through a cost-based tracking algorithm optimized to tolerate observational gaps and track quasi-stationary features. This census, representing the first systematic, high-resolution sea surface height anomaly (SSHA)-based characterization for this region, identified 344 persistent trajectories (≥14 days) and revealed a fundamental dichotomy in the Gulf’s dynamics: a transient, tidally dominated regime in the north (dominated by short-lived features) contrasting sharply with a persistent, topographically trapped regime in the south. Focusing on the long-lived population (lifetimes $>30$ days), our analysis confirms that multi-year, quasi-stationary cyclonic eddies are trapped in the southern basins, while a subset of energetic tracks exhibits coherent poleward propagation consistent with advection by the Mexican Coastal Current. Cyclonic structures dominate the ten longest-lived tracks (90%) and include two events with lifetimes confirmed to exceed 500 days. We also identify a robust seasonal decoupling between SSHA and sea surface temperature anomalies (SSTA) in spring, when surface heating masks the thermal signature of cyclones. This census, which documents multi-year structures and distinguishes the two regional regimes, establishes a new baseline for quantifying mesoscale transport and serves as a transferable framework for the new generation of satellite altimetry observations (i.e., the Surface Water and Ocean Topography, SWOT, mission). Full article

Wide-swath satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission provides an unprecedented opportunity to directly observe kilometer-scale ocean dynamics in two dimensions. In this study, we identify an atypical bimodal seasonal cycle of submesoscale processes in the Kuroshio Large Meander (KLM) region south of Japan using SWOT observations during 2023–2025. Submesoscale eddy kinetic energy (EKE) displays a pronounced winter maximum (December–January) as expected for midlatitude oceans, but also a distinct secondary maximum in late summer (August–September) that coincides with the Northwest Pacific typhoon season. SWOT-based eddy statistics reveal that cyclonic and anticyclonic eddies exhibit enhanced occurrence and intensity in winter and late summer. MITgcm LLC4320 outputs demonstrate that the late-summer EKE peak is primarily driven by typhoons, which rapidly deepen the mixed layer and intensify frontal gradients, leading to an intensification of submesoscale eddies. The Kuroshio path further modulates this response. During the KLM state, buoyancy gradients and mixed-layer available potential energy are amplified, allowing storm forcing to generate strong submesoscale activity. Together, typhoon forcing and current-path variability modify the traditionally winter-dominated submesoscale regime. These findings highlight the unique capability of SWOT to resolve submesoscale processes in western boundary currents during extreme weather events. Full article

The ODYSEA (Ocean DYnamics and Surface Exchange with the Atmosphere) mission will provide simultaneous two-dimensional measurements of currents and winds for the first time. According to the ODYSEA radar concept, with a high incidence angle, current noise is primarily driven by backscattered power, which is triggered by wind speed. Therefore, random noise will affect the quality of observations. In low wind conditions, the absence of surface roughness increases the noise level considerably, to the point where the measurement becomes unusable, as the error can exceed 3 m/s at 5 km posting compared to mean current amplitudes of tens of cm/s. Winds higher than 7.5 m/s enable current measurements at 5 km posting with an RMS accuracy below 50 cm/s, but derivatives of currents will amplify noise, hampering the understanding of ocean dynamics and the interaction between the ocean and the atmosphere. In this context, this study shows the advantages and limitations of using noise-reduction algorithms. A convolutional neural network, a UNet inspired by the work of the SWOT (Surface Water and Ocean Topography) mission, is trained and tested on simulated radial velocities that are representative of the global ocean. The results are compared with those of classical smoothing: an Adaptive Gaussian Smoother whose filtering transfer function is optimized based on local wind speed (e.g., more smoothing in regions of low wind). The UNet outperforms the kernel smoother everywhere with our simulated dataset, especially in low wind conditions (SNR << 1) where the smoother essentially removes all velocities whereas the UNet mitigates random noise while preserving most of the signal of interest. Error is reduced by a factor of 30 and structures down to 30 km are reconstructed accurately. The UNet also enables the reconstruction of the main eddies and fronts in the relative vorticity field. It shows good robustness and stability in new scenarios. Full article

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications. Full article

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms. Full article

The Global Navigation Satellite System (GNSS), Internet of Things (IoT) and cloud computing technologies enable high-precision positioning with flexible data communication, making real-time/near-real-time monitoring more economical and efficient. In this study, a multi-sensor GNSS-IoT system was developed for measuring precise water surface elevation (WSE). The system, which includes ultrasonic and accelerometer sensors, was deployed on a floating platform in Googong reservoir, Australia, over a four-month period in 2024. WSE data derived from the system were compared against independent reference measurements from the reservoir operator, achieving an accuracy of 7 mm for 6 h averaged solutions and 28 mm for epoch-by-epoch solutions. The results demonstrate the system’s potential for remote, autonomous WSE monitoring and its suitability for validating satellite Earth observation data, particularly from the Surface Water and Ocean Topography (SWOT) mission. Despite environmental challenges such as moderate gale conditions, the system maintained robust performance, with over 90% of solutions meeting quality assurance standards. This study highlights the advantages of combining the GNSS with IoT technologies and multiple sensors for cost-effective, long-term WSE monitoring in remote and dynamic environments. Future work will focus on optimizing accuracy and expanding applications to diverse aquatic settings. Full article

Surface water is the primary freshwater supply for Earth. Small lakes and ponds provide important ecological and economic services to society but are often left undocumented, or their documentation is outdated, due to their small sizes and temporal dynamics. This study tested the feasibility of the new Surface Water and Ocean Topography (SWOT) mission regarding the 3D documentation of small waterbodies in a coastal area of South Carolina, USA. Via deep learning using a recent 15 cm aerial image, small waterbodies (>0.02 ha) were extracted at an average precision score of 0.81. The water surface elevation (WSE) of each waterbody was extracted using the SWOT Level-2 Water Mask Pixel Cloud (PIXC) product, with the data collected on 1 June 2023. Using a statistical noise-removal approach, the average WSE values of small waterbodies revealed a significant correlation (Pearson’s r = 0.64) with their bottom elevations. Via spatial interpolation, the water levels of small waterbodies across the study area were generally aligned with the state-reported Cone of Depression of ground water surfaces in underlying aquifers. While the WSE measurements of SWOT pixel points are noisy due to the land–water interactions in small waterbodies, this study indicates that the SWOT PIXC product could provide a valuable resource for assessing freshwater availability to assist in water-use decision-making. Full article

Traditional methods for the construction of intertidal digital elevation models (DEMs) require the integration of long-term multi-sensor datasets and struggle to capture the spatiotemporal variation caused by ocean dynamics. The SWOT (surface water and ocean topography) mission, with its wide-swath interferometric altimetry technology, provides instantaneous full-swath elevation data in a single pass, offering a revolutionary data source for high-precision intertidal topographic monitoring. This study presents a framework for SWOT-based intertidal DEM extraction that integrates data preprocessing, topographic slope map construction, and tidal channel masking. The radial sand ridge region along the Jiangsu coast is analyzed using SWOT L2 LR (Low Resolution) unsmoothed data from July 2023 to December 2024. Multisource validation data are used to comprehensively assess the accuracy of sea surface height (SSH) and land elevation derived from LR products. Results show that the root mean square error (RMSE) of SSH at Dafeng, Yanghe, and Gensha tide stations is 0.25 m, 0.19 m, and 0.32 m, respectively. Validation with LiDAR data indicates a land elevation accuracy of ~0.3 m. Additionally, the topographic features captured by LR products are consistent with the patterns observed in the remote sensing imagery. A 16-month time-series analysis reveals significant spatiotemporal variations in the Tiaozini area, particularly concentrated in the tidal channel areas. Furthermore, the Pearson correlation coefficient for the DEMs generated from SWOT data decreased from 0.94 over a one-month interval to 0.84 over sixteen months, reflecting the persistent impact of oceanic dynamic processes on intertidal topography. Full article

The Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA and several international collaboration agencies, aims to achieve high-resolution two-dimensional observations of global surface water. Equipped with the advanced Ka-band radar interferometer (KaRIn), it significantly enhances the ability to monitor surface water and provides a new data source for obtaining large-scale water surface elevation (WSE) data at high temporal and spatial resolution. However, the accuracy and applicability of its scientific data products for inland water bodies still require validation. This study obtained three scientific data products from the SWOT satellite between August 2023 and December 2024: the Level 2 KaRIn high-rate river single-pass vector product (L2_HR_RiverSP), the Level 2 KaRIn high-rate lake single-pass vector product (L2_HR_LakeSP), and the Level 2 KaRIn high-rate water mask pixel cloud product (L2_HR_PIXC). These were compared with in situ water level data to validate their accuracy in retrieving inland water levels across eight different regions in the middle and lower reaches of the Yangtze River (MLRYR) and to evaluate the applicability of each product. The experimental results show the following: (1) The inversion accuracy of L2_HR_RiverSP and L2_HR_LakeSP varies significantly across different regions. In some areas, the extracted WSE aligns closely with the in situ water level trend, with a coefficient of determination ($R^2$) exceeding 0.9, while in other areas, the $R^2$ is lower (less than 0.8), and the error compared to in situ water levels is larger (with Root Mean Square Error (RMSE) greater than 1.0 m). (2) This study proposes a combined denoising method based on the Interquartile Range (IQR) and Adaptive Statistical Outlier Removal (ASOR). Compared to the L2_HR_RiverSP and L2_HR_LakeSP products, the L2_HR_PIXC product, after denoising, shows significant improvements in all accuracy metrics for water level inversion, with $R^2$ greater than 0.85, Mean Absolute Error (MAE) less than 0.4 m, and RMSE less than 0.5 m. Overall, the SWOT satellite demonstrates the capability to monitor inland water bodies with high precision, especially through the L2_HR_PIXC product, which shows broader application potential and will play an important role in global water dynamics monitoring and refined water resource management research. Full article

The Baltic Sea is an intra-continental marginal sea that is vertically stratified with a strong halocline isolating the saline bottom layer from the brackish surface layer. The surface layer is eutrophic, and abiotic zones lacking oxygen are common in the deeper regions. While freshwater is constantly flowing into the North Sea, oxygen-rich bottom waters can only occasionally enter the Baltic Sea following a special sequence of transient weather conditions. These so-called Major Baltic Inflow events can be monitored via the sea level gradients between the Kattegat and the Western Baltic Sea. Innovative interferometric altimetry from the Surface Water and Ocean Topography (SWOT) mission gave us the first opportunity to directly observe the sea level signal associated with the inflow event in December 2023. Recent high-rate multi-mission nadir altimetry observations support the SWOT findings for scales larger than 50 km. The SWOT observations are compared to the simulations with the regional 3D HBMnoku ocean circulation model operated by the German Federal Maritime and Hydrographic Agency (BSH). The model explains more than 80% of the variance observed by SWOT and up to 90% of the variance observed by the nadir altimeters. However, the north–south gradients of the two datasets differ by about 10% of the overall gradient. Comparisons with tide gauges suggest possible model deficiencies on daily to sub-daily time scales. In addition, the SWOT data have many fine scale structures, such as eddies and fronts, which cannot be adequately modeled. Full article