SUMMARY: The wide-swath altimeter satellite Surface Water and Ocean Topography (SWOT) will provide high spatiotemporal resolution sea surface heights (SSHs), which is crucial for studying the impact of observation errors on marine gravity recovery. This study uses simulated SWOT data to derive deflection of the vertical (DOV) and gravity anomalies in the northern South China Sea. We quantified the impact of SWOT errors on DOV and gravity anomalies, and analysed the contributions from different directions of geoid gradient. The results show that the geoid gradient in the cross-track direction significantly improves gravity field recovery by enhancing the precision of east component of DOV. For one-cycle SWOT observations, phase errors emerge as the most impactful error affecting both DOV and gravity anomalies, followed by random errors. 2-D Gaussian filtering and the tilt correction proposed in this study could effectively mitigate their impact. Using the corrected data for DOV computation, the precision in the east and north components improves by 75.32 and 46.80 per cent, respectively, while enhancing the accuracy of the gravity field by 70.23 per cent. For 17-cycle data, phase errors and random errors remain the predominant factors affecting DOV and gravity anomalies, but their impact diminishes with an increase in SWOT observations. Our results indicate that marine gravity accuracy improves by approximately 70 per cent compared to a single cycle. Whether for single-cycle or multicycle data, the impact of phase errors is roughly twice that of random errors. These data processing strategies can serve as valuable references for wide-swath altimeter data processing, aiming to advance the precision and resolution of marine gravity field recovery.

The Surface Water and Ocean Topography (SWOT) mission, initially designed to observe oceans and inland waters, proves valuable for mapping intertidal flat topography. This study presents a fusion method that integrates freely available SWOT interferometric altimetry data with Sentinel‐2 imagery. The density‐based spatial clustering of applications with noise algorithm is employed to accurately identify observation data located in the intertidal zone. Intertidal topography with a 10 m resolution were generated from L2_HR_PIXC data collected during SWOT's science phase (July 2023–October 2024) in the intertidal region along the coast of Jiangsu Province, China. These Digital Elevation Models were compared with laser altimetry observations from the ICESat‐2. SWOT observations show high accuracy, with RMSEs of 0.24 m (multi‐cycle) and 0.41 m (single‐cycle), confirming their potential for intertidal monitoring. This study presents a data processing strategy that does not rely on ground‐based observations, demonstrating potential for application in a broader range of regions.

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions.

We assess the accuracy of the Surface Water and Ocean Topography (SWOT) mission for deriving marine gravity anomalies and bathymetry in the Philippine Sea using wide-swath L3 Ka-band radar interferometer (KaRIn) data. Our evaluation includes determining the deflection of vertical (DOV) using the least squares collocation (LSC) method, recovering gravity anomalies with the inverse Vening-Meinesz (IVM) formula, and predicting bathymetry through the gravity-geological method (GGM). The accuracy of the east-west DOV component has improved but remains lower than that of the north-south component. SWOT-derived (from 14-cycle data) gravity anomalies exhibit stronger signals in the short-wave ranges (< 20 km), with a precision of 2.37 mGal compared to shipborne measurements. The SWOT-derived bathymetry shows an accuracy of 124.02 m, representing an 8.36 m improvement compared to the conventional SIO altimeter-based model. We show that 14 cycles (10 months) of SWOT data provide more detailed information than 30 years of traditional nadir altimetry in both gravity anomaly and bathymetry. We have optimized the SWOT data processing strategy to enable the retrieval of highquality marine gravity anomalies. With advancements in data processing and the accumulation of observations, the SWOT mission is progressing toward its marine geophysics objectives.

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10⁹ m³ from July 2002 to December 2022, with a growth rate of 5.3766 × 10⁸ m³/a. The growth rate from January 2005 to January 2015 is 4.4850 × 10⁸ m³/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10⁸ m³/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.