Although virtual stations constructed from reprocessed Jason altimetry datasets between 2002 and 2022 have offered a unique opportunity for estimating coastal sea level variations, the denser spatial coverage and longer time span of virtual stations are still in high demand. To achieve this goal, three different sets of virtual stations are constructed in this study, which are 1) the Jason-series reprocessed Low Resolution Mode (LRM)+Sentinel-6 MF official Synthetic Aperture Radar Mode (SARM) over January 2002 and March 2025; 2) the Jason-series+Sentinel-6 MF reprocessed LRM over January 2002 and March 2025; and 3) the ERS-2+Envisat reprocessed LRM over May 1995 and October 2010. The different altimeter combinations provide a good opportunity to investigate the performance of ERS-2/Envisat and SARM altimeters in observing the coastal sea level trends. The validation results confirm the good agreement of sea level trends between Sentinel-6 MF reprocessed LRM and SARM on a global scale (0.2 +/- 1.3 mm yr-1). Moreover, the global-averaged trend difference of Jason-series reprocessed LRM+Sentinel-6 MF SARM virtual stations against tide gauges achieves 0.1 +/- 1.1 mm yr-1. However, the ERS-2 and Envisat virtual stations degrade significantly considering the large mean trend deviation from the collocated tide gauges (-2.3 +/- 1.9mm yr-1), and from the Jason-series+Sentinel-6 MF SARM virtual stations (0.8 +/- 6.1 mm yr-1) at crossover points. The reasons behind this are due mainly to their lower temporal resolution (similar to 35 days vs similar to 10 days) and inferior data quality. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Abstract This study examines the backwater effects from ice jams in Arctic rivers using the Surface Water and Ocean Topography (SWOT) satellite. Ice jams occur when chunks of ice accumulate and obstruct the river flow, causing rapid water level rises and flooding. The ice cover on the Klondike River (April 2024) and the Peace River (April 2023) is identified from optical images and compared with SWOT‐derived water surface elevations (WSE). Estimated water surface slope from high‐resolution river elevation profiles reveal notable backwater effects upstream of the ice jams; water surface slopes decrease markedly (Peace: from 80 to 10 cm/km and Klondike: from 300 to 150 cm/km). The Peace River profiles are compared with ICESat‐2 and Sentinel‐3 measurements, highlighting the advantages of SWOT's spatial and temporal resolution. SWOT's ability to resolve WSE profiles in rivers demonstrates its value for quantifying Arctic ice jams, improving both hydraulic process understanding and flood risk assessment.

The distribution and variation of ocean temperature are closely associated with ocean dynamic processes. Highprecision ocean subsurface temperature fields are critical for studies on ocean dynamics and climate change. While satellite remote sensing provides extensive data on sea surface temperature, it lacks direct observations of ocean subsurface temperatures. In-situ measurements typically result in sparse and uneven spatiotemporal data coverage. Consequently, obtaining accurate high-resolution ocean subsurface temperature data remains a significant challenge in marine science. This study introduces a novel ocean subsurface temperature reconstruction model based on Spatiotemporal Graph Attention Networks (STGAT). STGAT is capable of inferring subsurface temperature fields from multiple satellite-derived sea surface observations, including Sea Level Anomaly (SLA), Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and Sea Surface Wind (SSW), as well as the derived Depth-Specific Temperature Anomaly Gradient (DSTAG). The Kuroshio Extension region in the Northwest Pacific, characterized by intense ocean dynamic activity, was selected as the experimental area. Ocean temperature fields at 21 depth levels (20-1941 m) for 2022 were reconstructed using satellite sea surface observations from 2017 to 2022. The accuracy of the reconstructed subsurface temperature fields was evaluated using the GLORYS12V1 reanalysis datasets and the EN4 in-situ observation datasets. Comparative analysis with reanalysis data demonstrates that the proposed model effectively captures the spatiotemporal characteristics of ocean temperature across all depth levels, accurately reflecting realistic spatial distributions throughout the water column. The reconstructed subsurface temperature fields achieved mean RMSE and R2 values of 0.916 degrees C and 0.866, respectively. Validation against EN4 data further confirms that the model's ability to reproduce vertical thermal variations, with mean RMSE and R2 values of 0.898 degrees C and 0.976, respectively. When compared with other machine learning-derived models, the STGAT model demonstrates superior performance in terms of reconstruction accuracy. In conclusion, the STGAT model effectively addresses the challenges posed by complex and highly turbulent oceanic processes, offering a promising new approach for retrieving high-resolution and high-accuracy ocean subsurface temperature data.

The 3-D ocean temperature field plays a critical role in studies of global climate change, carbon cycling, and ocean dynamics. Subsurface temperature reconstruction, particularly within the thermocline, remains insufficient due to limitations in observational coverage and modeling frameworks. To address this challenge, we propose the convolutional neural network-Swin Transformer spatial parallel Convolutional Long Short-Term Memory (CSSP-ConvLSTM), a subsurface temperature reconstruction model that integrates multisource sea surface remote sensing data. The model employs a spatial dual-branch structure and a temporal enhancement mechanism to achieve multiscale characterization of local details, global dependencies, and nonlinear evolution. When applied to the northwestern Pacific (30-1000 m), reconstruction accuracy is significantly improved compared with single-branch or single-model baselines, with an average R-2 of 0.9811 and root mean squared error (RMSE) of 0.4557 degrees C; reconstruction errors are markedly reduced within the thermocline (100-600 m) and deep layers (800-1000 m). Independent evaluations using ARMOR3D and EN4 datasets are conducted, confirming strong temporal generalization, stability, and physical consistency. Interpretability analysis based on SHAP indicates that shallow temperatures are primarily influenced by sea surface temperature (SST) and sea surface height (SSH), whereas subsurface and deep layers are more affected by sea surface salinity (SSS) and SSH. These results demonstrate that the CSSP-ConvLSTM provides an effective approach for reconstructing high-resolution 3-D ocean temperature fields and facilitates improved understanding of thermocline dynamics and their potential climatic implications.

Abstract Despite millions of ship soundings, bathymetry from satellite derived gravity is still necessary to fill in approximately three quarters of the global oceans. The methods used to carry out this inference have not changed significantly since the 1990s; however new methodology involving Machine Learning (ML) improves the bathymetric predictions considerably. Here we utilize five independent ML models from a workshop at the Technical University of Denmark (DTU). We highlight the benefits achieved by either (a) inclusion of a new highly‐accurate gravity field from the Surface Water and Ocean Topography (SWOT) satellite (∼22% improvement in spatial resolution), or (b) highly flexible ML methods capable of inferring bathymetric regimes not previously possible. By taking advantage of these five independent models we can determine regions of high confidence in our bathymetric inversion as well as regions with challenging conditions. Building on model prediction confidence and features revealed from the dense gravity field obtained by SWOT, we present a global features‐of‐interest map that, if mapped by ship soundings, would yield the largest improvement of the global bathymetry. These features are primarily located in regions with sparse multibeam coverage and associated with large gravity anomalies in the marine gravity field, indicating a potential presence of complex seafloor topography.

Abstract Only one quarter of the global ocean floor has been directly surveyed; the remaining three quarters are inferred from satellite altimeter‐derived gravity data using techniques developed in the 1990s. These classical methods correlate gravity anomalies with known depths and extrapolate bathymetry in unsounded regions. However, spatial resolution has remained limited to 12 km full wavelength due to the smoothing effects of upward continuation. Two recent advances are now reshaping this field: the Surface Water and Ocean Topography mission has increased radar altimeter range precision by a factor of four, effectively doubling the two‐dimensional resolution, and modern machine learning (ML) approaches allow for scalable, high‐fidelity inversion. The authors of this paper organized a workshop, initially at the Technical University of Denmark, that brought together experts in marine gravity, seafloor mapping, and ML to tackle this challenge. Here, we present results from five independent research groups demonstrating consistent and substantial improvements (23%–46%) in bathymetric prediction accuracy, each incorporating the fundamental physics of downward continuation within advanced data‐driven modeling frameworks. In addition to improving the accuracy and resolution of the predicted bathymetry, each of the five models provides a substantial improvement in depth accuracy at shallow seamounts and deep trenches.

Satellite altimetry is instrumental in deciphering the dynamics of oceans and coastal regions. It yields indispensable data critical for monitoring global sea levels, predicting wave heights, and charting the courses of ocean currents and river elevations. These insights are pivotal for advancing climate research, ensuring navigational safety, and managing water resources effectively. Nonetheless, in coastal settings, the efficacy of conventional altimetry is constrained by its spatial resolution and the interference of land in the radar signal near the coastlines. These limitations hinder its ability to accurately capture the nuanced characteristics of dynamic and intricate coastal environments.The launch of the Surface Water and Ocean Topography (SWOT) satellite represents a monumental leap in the technology of satellite altimetry. With advanced high-resolution wide swath altimetry and innovative use of the phase difference between dual onboard antennas, SWOT drastically reduces the limitations of traditional radar altimeters. SWOT provides a 2D measurements grid with a detailed 50m grid spacing not degrading towards the coast. This marks a substantial enhancement compared to the 7-km across-track spacing along a 1D trajectory offered by conventional altimetry.This enhancement allows for the precise and detailed monitoring of dynamic coastal phenomena such as tides and tidal bores, even in estuaries.Tidal bores, characterized as sudden and powerful water surges against the river's current, are critical for local ecology, navigation, and flood management. Despite their importance, their dynamic and transient nature has made them challenging to study using conventional methods. The Bristol Channel, with its extreme tidal range and the presence of the Severn Bore, presents an ideal case study to demonstrate SWOT's capabilities.We use SWOT 50-meter pixel cloud data during the 1-day fast sampling repeat period in April 2023 to study the high-resolution tidal signal in the Bristol Channel - Severn Estuary and the Severn tidal bore. The results demonstrate that SWOT can capture both complex tidal signals associated with wetting and drying close to the coast, but also the tidal bore sweeping up the Severn River from the mouth of the river and some 20 km upstream.

Since the beginning of the precision satellite altimeter era in the early 1990s, efforts have been focused on computing the mean height of the ocean surface for use in various geodetic and oceanographic studies. With 30 years of satellite measurements now available, it is time to rethink how we model the mean sea surface (MSS) in the era of climate change.There are linear changes in the height of the ocean surface due to melting ice and increasing ocean heat content that will not average to zero when computing the mean. Today, there are places in the ocean that are 15 cm higher than they were at the start of the altimetric era some 30 years ago. Today, conventional MSS models like CLS15/22 or DTU15/21 are roughly 5 cm lower than what is observed by present-day satellites like Sentinel6-MF.We propose that linear sea level changes are estimated simultaneously and consistently with the mean sea surface computation and added to the definition of the MSS, which is tied to a particular date in time. This is possible because the MSS are tied to the 2003.01.01 period for the DTU MSS models. We also investigated the acceleration of sea surface height but found these small and still unstable [Nerem et al., 2018]. We also found that these are still somewhat dependent on the Side A correction of the TOPEX mission. We conclude that a longer time series is needed before a stable map of the accelerations can be computed and applied.There is considerable evidence that using a 30-year trend pattern in sea surface height is stable and is driven by the “forced response” of Greenhouse gases and aerosols. These patterns will be reasonably persistent as we move forward in time.Testing a new DTU23MSS mean surface tailored to the year 2023 to our processing of the recently available 2023 SWOT data, we find this new DTU23MSS reduces the spatial variability of the SWOT data which is important to the processing and particularly the roll-error correction applied to the 2D SWOT sea surface height data. Applying the new DtU21MSS to conventional satellites like Sentinel-3A/B and 6 reduces both offset and spatial variability of the data indicating that the new MSS is actually very close to a “present-day mean”

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.

Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches such as shipborne sounding and airborne bathymetric light detection and ranging (lidar) surveys are expensive, time-consuming, and constrained in politically sensitive regions. Moreover, satellite-altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open-source, and cost-effective technique for comprehensively characterizing the bathymetry of a region. Given the scarcity of in situ water depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS (Hohai University 2024 Shallow-Water Depth Model of South China Sea), was developed using a linear band model by integrating 1298 Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers over 120 islands and reefs in the SCS region at a resolution of 10 m. Validation against independent ICESat-2 depth data yielded a root mean square error for the model of 0.53–1.24 m (< 5 % of the maximum depth). Further validation using independent airborne lidar bathymetry data in the Lingyang Reef demonstrated an accuracy of 1.01 m. Comparisons with existing bathymetry models revealed the superior performance of the developed model. While traditional bathymetry models exhibit errors of up to tens of meters or larger over island regions and should therefore be used with caution, HHU24SWDSCS demonstrated good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. The HHU24SWDSCS data are freely available at https://doi.org/10.5281/zenodo.13852568 (Wu et al., 2024a).

Elastic vertical land movement (eVLM) is the lithosphere's immediate elastic response to the loading and unloading of the Earth's surface mass. Understanding eVLM is crucial for interpreting relative sea level changes, particularly in coastal regions where subsidence or uplift can significantly alter the impacts of sea level changes recorded by tide gauges. Here we present a comprehensive global eVLM model, offering valuable insights for geodesy and related fields, especially in assessing observations from tide gauges and GNSS. Our eVLM model spans from 1900 to 2022, featuring a 0.5-degree spatial resolution. It provides annual data from 1900 to 1990 and monthly data from 1991 to 2022, enabling both long-term and seasonal assessment. The dataset is available in three different reference frames: Centre of Mass (CM), Centre of Figure (CF), and ITRF2020, and thus suitable for many geodetic applications. This study incorporates mass change estimations from Greenland, Antarctica, global glaciers, and land water storage (LWS), divided into natural LWS variations and anthropogenic water management like groundwater depletion and dam retention. Thus, we can explain regional VLM patterns that cannot be solely attributed to Glacial Isostatic Adjustment (GIA) models, for example, subsidence across Australia or uplift in Scandinavia that is larger than modeled GIA. Methodology: We employed a composite loading model, integrating ice models from Greenland (Mankoff et al., 2021) and Antarctica (Otosaka et al, 2022; Nilsson et al, 2022) and glacier models (Hugonnet et al., 2022), GRACE observations, and a land water storage model (Müller-Schmied et al, 2023). Each of the aforementioned five causes of eVLM was perturbed with its uncertainty a thousand times, and the sea level equation was resolved for each variant using the ISSM-SEESAW framework (Adhikari et al., 2016). To align the results with observations in the ITRF2020 reference frame, which mirrors CM on secular timescales and CF on non-secular timescales (Dong et al, 2003). To accommodate this, we applied CM and CF Love loading numbers (Blewitt, 2003) in our calculations, enabling analysis in all three reference frames.

Studying ocean tides from satellite altimetry has traditionally been difficult in coastal regions, mainly due to the complexity of tides in these regions, limited spatial coverage, and land contamination of the radar returns. The Cal/Val phase and the science orbit phase of SWOT provide unique observations which can be exploited for tidal analysis. The nadir data provided by this mission complements other traditional altimetry missions and will serve the refinement of global ocean tide models well in future studies. The KaRIn data, however, is beneficial for evaluating the spatial variability of ocean tides at much smaller scales than previously possible from altimetry or in-situ measurements. In addition, areas very close to the shoreline can also be monitored. Analysing tides in complex coastal regions, such as fjords and inlets, is now also possible thanks to the increased spatial coverage of SWOT. This presentation evaluates the pixel cloud data of the hydrological product and the ocean product provided by SWOT in three regions. These regions are selected to provide examples of the usefulness of these data in very complex environments. The regions are as follows:The Bristol Channel, on the west coast of the UK. The Sognefjord along the west coast of Norway. The Long Island Sound on the east coast of the USA. These regions have relatively large tidal ranges and have been challenging for conventional altimetry, resulting in reduced accuracy in available ocean tide models. These regions are also well covered by in-situ measurements and are either covered by the Cal/Val phase or the nominal orbit of the SWOT mission. The resultant estimations will be contrasted with in-situ measurements and state-of-the-art global models.

This paper presents BathDNN25, a global bathymetry model developed using gravity data derived from wide-swath altimetry collected by the Surface Water and Ocean Topography (SWOT) mission, with shipborne bathymetry serving as training data in a deep neural network (DNN) framework. BathDNN25 integrates multiple geophysical inputs, including gravity anomalies , vertical gravity gradients , their band-pass filtered forms , the north and east components derived from the deflection of the vertical (, ), their band-pass versions (, ), low-pass filtered bathymetry , and both low-pass and band-pass filtered gravity (, ), to capture both large-scale trends and fine-scale bathymetric features. A key innovation lies in its use of multi-scale geophysical features, enabling enhanced sensitivity to morphological complexity such as ridges, escarpments, and seamounts, while adapting well to varying geological conditions and data sparsity. Model performance was assessed using residual statistics against independent data sets, including global shipborne soundings and seamount summits, with BathDNN25 achieving residual standard deviations of 99 and 167 m, respectively. Compared to existing methods (Harper & Sandwell, 2024, https://doi.org/10.1029/2023ea003199), this represents reductions in residual error of over 51% and 113%. SHAP analysis across 14 regions and ablation tests using four model variants further confirmed the complementary value of SWOT-derived gravity features. Overall, BathDNN25 demonstrates accuracy, robustness, and scalability, underscoring the importance of high-quality geophysical inputs and the potential of SWOT-derived data and artificial intelligence in advancing global bathymetric modeling.

Satellite altimetry has been the major data source for marine geoid determinationand gravity recovery in recent decades. In general, altimetry-derived geoid and gravity anomaly models are typically released with a 1' x 1' gridding interval. However, their actual spatial resolution is far lower than the nominal similar to 2 km level. Therefore, analyzing the marine geoid resolution capability from satellite altimetry observations is crucial for marine gravity recovery studies. The Surface Water and Ocean Topography (SWOT) Mission is a newly launched satellite using advanced radar technology to make headway in observing thevariability of water surface elevations, providing new information through along-track and across-track two-dimensional swath observations. Here, we present the analysis results of marine geoid resolution capability for both typical conventional nadir altimeters and the SWOT Ka-band radar interferometer (KaRIn) in 2 degrees x 2 degrees bins worldwide between 60 degrees N and 60 degrees S. We demonstrate the potential of SWOT KaRIn to capture along-track short-wavelength signals below 10 km and analyze the bin-based statistics of key marine geophysical factors correlated with this marine geoid resolution capability. Generally, SWOT KaRIn exhibits better marine geoid resolution capability over bins with large-scale seamounts or trenches.

The potential of using airborne gravity gradient tensor (GGT) for coastal quasi-geoid (QG) refinement is explored, and the contributions introduced from individual GGT components and their combinations are quantified and evaluated. High-resolution QGs, with a spatial resolution of similar to 0.5 km, are computed over St. George's Bay in southwestern Newfoundland, Canada. The findings indicate that fully focused synthetic aperture radar (FFSAR) and surface water and ocean topography (SWOT) altimetry data are effective in differentiating the performance of various QGs in coastal areas. The application of the vertical gravity gradient (VGG) obtains the highest quality QG when utilizing individual GGT components. The combination of two or more components results in improved QGs compared to the results derived from individual components. The integration of full GGT yields the best QG, with a standard deviation (SD) of misfits against Sentinel-3A FFSAR (SWOT) altimetry data being 1.13 (2.49) cm, representing reductions of 28.48%-53.50% (5.32%-15.02%) compared to results derived from individual GGT components. Comparisons of the QG computed by fusing full GGT with the Canadian gravimetric QG CGG2013 and high-degree global geopotential models (GGMs) further underscore the advantages of using GGT in QG modeling, revealing SD reductions of 55.16%-65.02% (7.55%-33.95%) against FFSAR (SWOT) altimetry data. These findings underscore the effectiveness of using airborne GGT in coastal QG modeling, particularly in recovering short-wavelength signals and addressing challenges in satellite altimetry over coastal environments. In addition, this study highlights the superiority of using full GGT over individual components in QG modeling.

Observations from satellite altimeters are essential to mapping the marine gravity field and bathymetry. As most of the ocean basins are yet to be mapped by sonar, obtaining reliable data of sea surface height and sea surface slopes is key to improving our understanding of the marine gravity field and bathymetry. Improvement in altimeter systems has enabled the marine gravity field to be determined to a few mGal, however with conventional satellite altimetry, improvements are challenging. The major challenge is the sampling geometry of conventional satellite altimeters, with along-track (majorly north-south) sea surface slopes being much better determined than across-track slopes (east-west). With the KaRIn instrument on the Surface Water and Ocean Topography (SWOT) satellite, swath-altimetry with 2-dimensional observations of the sea surface height is possible. From these observations, the directional sea surface slopes in both along-track and across-track can be determined. However, determining the resolution and precision with which the sea surface slope is determined, is of fundamental importance for the improvement of the mapping of the marine gravity field. We present directional sea surface slopes associated with a major seamount using SWOT L2 data and with a minor seamount using SWOT pixel-cloud data, demonstrating the quantum leap forward possible with SWOT. With three parallel beams, ICESat-2 is another satellite that can determine the east-west sea surface slope. From observing the difference in sea surface height between beams, we can determine the directional sea surface slopes in north-south and east-west components, with auspicious results. With data from ICESat-2, we aim to validate the SWOT directional sea surface slopes at cross-overs between SWOT and ICESat-2 and determine the relative initial performance.

The fully focused synthetic aperture radar (FFSAR) altimetry technology has greatly enhanced the accuracy of coastal sea surface height (SSH). However, due to its pulse-limited mode in the cross-track direction, FFSAR is still susceptible to land interference. Additionally, the spatiotemporal resolution of in situ data limits the evaluation of altimetric SSH in both coastal and open ocean regions. This study proposes a waveform interference detection and removal algorithm (WIDA), which utilizes waveform differencing and azimuthal constant false alarm rate (CFAR) techniques to identify and reject interference signals in island areas. Tide gauge data, particularly a seamless and high-resolution airborne gravimetric quasi-geoid (QG) model, are employed to validate the performance of WIDA, providing a new perspective for the continuous and consistent evaluation of SSH profiles. Validation of five years of data from six Sentinel-3A/B tracks over the Xisha Islands in the South China Sea demonstrates that FFSAR waveforms retracked by WIDA (FFSAR_WIDA), with 80-Hz sampling, significantly outperform FFSAR_coastal retracker for SAR altimetry (CORAL), unfocused SAR (UFSAR), and UFSAR_WIDA within 5 km of the tide gauge station. The standard deviations (SDs) for FFSAR_WIDA, FFSAR_CORAL, UFSAR, and UFSAR_WIDA are 0.153, 0.192, 0.367, and 0.173 m, respectively. Moreover, the evaluation with the QG also reveals a marked improvement over FFSAR_CORAL and UFSAR within 5 km from the islands. The spatial distribution assessment shows that as the distance from the island decreases, the SD for FFSAR_CORAL (UFSAR) increases from 0.010 m (0.011 m) at 15 km to 0.061 m (0.089 m) at 3 km, while FFSAR_WIDA (UFSAR_WIDA) remains consistently below 0.011 m (0.014 m).

Bathymetry provides instrumental information for studying sedimentary processes, global climate change, and benthic morphologies. The advantages and applicabilities of different techniques for bathymetry detection vary. We propose a framework for bathymetry enhancement from multisource data based on spherical radial basis functions (SRBFs). A case study is conducted over the Paracel Islands in South China Sea (SCS), where Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, airborne gravimetric observations, echo soundings, and the reference model DTU18BAT are merged. Numerical results suggest that the fusion of ICESat-2 observations dramatically enhances the quality of the computed bathymetry model near the islands, the root-mean-squared error (RMSE) of which is reduced by 45.35%-67.95% compared to existing models when validated against the satellite-derived bathymetry (SDB) with decimeter-level accuracy. By additionally fusing the airborne gravimetric data, bathymetry is further enhanced by similar to 22.49%, particularly over islands with sparse ICESat-2 trajectories. Comparisons with surveyed airborne bathymetric lidar data over the northern Antelope Reef yielded results consistent with those obtained from the SDB, suggesting that SDB is possible to serve as control data in waters devoid of ground truth data. Further analysis reveals that the models constrained by echo soundings performed better than existing models in deep waters, with reductions of 17.79%-44.99% in terms of RMSE. By fusing airborne gravity data, bathymetry is improved by similar to 10%, highlighting the utilization of airborne gravimetry in both shallow and deep waters. The proposed SRBF approach offers an effective way to merge heterogeneous data for high-quality bathymetry determination.

The total mass change of the Earth's land surface precisely offsets the combined changes in the atmosphere and oceans, resulting in a net-zero change for the entire system (land+ocean+atmosphere). Closing the ocean mass budget is crucial for understanding current and future sea-level changes. Recent efforts to reconcile ocean mass observations from GRACE and GRACE-Follow On satellites (hereafter unitedly referred to as ‘GRACE’) with both steric-corrected altimetry and and land/ice to ocean estimates have revealed a discrepancy in the mass budget (Wang et al, 2022; Barnoud et al, 2022). This finding indicates a concerning misalignment in our global observation system or understanding of earth mass transport. This study uses GRACE-independent estimates/models of land surface mass changes to validate 20 years of GRACE observations. By calculating the monthly Gravitational, Rotational, and Deformational (GRD) response to 20 years of land mass changes, we reconstruct the global, regional, and seasonal ocean mass changes observed by GRACE from 2003 to 2022. Over the 20-year period, the ocean mass reconstruction aligns well with the GRACE observations. However, a significant deviation emerges after 2020, with the reconstruction showing a larger ocean mass change than GRACE. We demonstrate that this deviation is likely caused by an underestimation of Western Africa precipitation in the ERA5 reanalysis, commonly used by hydrological models to estimate changes in land water storage. Land mass observations from GRACE further confirmvthis underestimation and shows great alignment between models and observations when excluding sub-Saharan Africa. Our results show a global agreement between GRACE and GRD-induced ocean mass changes, suggesting that the misalignment between GRACE and steric-corrected altimetry is likely due to errors in the ARGO observing system. A reported 'salinity-drift' is the primary source of error, and together with an error in the wet path delay originating from drift in the radiometer of the Jason-3 satellite explains most of the post-2016 difference between GRACE and steric-corrected altimetry is identified. The remaining differences likely originate from GIA and/or Argo-biases.