Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone.

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean.

Synthetic aperture radar (SAR) provides high-resolution observations of radial sea surface currents. By combining radial currents observed from different viewing directions, the trajectory-crossing method can reconstruct the sea surface current vector field. In practice, observations from ascending and descending passes are acquired at different times, so the spatial structure of the surface current field may evolve between acquisitions. Directly combining multitemporal observations can therefore introduce spatial mismatch and reduce reconstruction accuracy. This study proposes a trajectory-crossing method for reconstructing sea surface current vectors from multitemporal Sentinel-1 Doppler observations. Maximum cross-correlation (MCC) is applied to gradient images of the radial current fields to estimate displacement and spatially register observations acquired at different times before vector reconstruction. The method is evaluated using Sentinel-1 data acquired over the Gulf Stream region and compared with geostrophic currents from the Copernicus Marine Environment Monitoring Service (CMEMS), Surface Water and Ocean Topography (SWOT) observations, and Global Drifter Program (GDP) drifter measurements. Results show that the proposed method reduces mismatch effects and improves the accuracy and stability of sea surface current vector reconstruction. It provides a practical approach for deriving surface current vector fields from multitemporal SAR Doppler observations.

Coastal bathymetric information is essential for marine engineering, navigation safety, and coastal management. Coastal bathymetric inversion based on wave characteristics extracted from synthetic aperture radar (SAR) images is a widely used technique. Existing studies that apply this method have mostly focused on long-period wave-dominated areas. However, in short-period wave-dominated areas, this method suffers from large errors and poor stability, limiting its practical application. To address this problem, this study proposes a hierarchical fusion method for SAR-based coastal bathymetric inversion in short-period wave-dominated areas. The proposed method significantly reduces bathymetric inversion errors by hierarchically fusing multi-angle wavelength estimation results and multi-temporal SAR bathymetry inversion results, thereby achieving more robust underwater topography mapping. A validation experiment was conducted on the Wengtian coast, Hainan Island. Compared with the best single-temporal inversion results, the proposed method reduces the MAE by 19.5% and the RMSE by 23.5%. Finally, the effects of the dispersion relation and the selection of the fusion strategy on the bathymetric inversion results are discussed. These results show that the proposed method significantly improves bathymetric inversion accuracy in short-period wave-dominated areas.