Satellite sea surface salinity (SSS) observations play a critical role in the study of ocean circulation and climate regulation. However, mesoscale salinity dynamics (e.g., eddies, fronts) remain poorly resolved by current salinity satellites, such as Soil Moisture and Ocean Salinity (SMOS), due to their low effective resolution (>100 km). To address this, we proposed the SMOS Sea Surface Salinity Super-Resolution Reconstruction (S5R2) network. This deep learning framework achieved superresolution (SR) reconstruction of the SMOS L3 SSS product from 1/4° to 1/12° by fusing multivariate satellite observations. Our approach integrated a land filtering mechanism into a hybrid Transformer-CNN architecture, enhancing both global and local attention to ocean dynamics while suppressing interference from land-based information. Meanwhile, we improved the search efficiency of the optimal subset of input variables by guiding the search direction and step size using prior knowledge. The results demonstrated that S5R2 outperformed existing L3 and L4 satellite SSS products and mainstream SR algorithms. Compared to the input SMOS L3 SSS product, S5R2 achieved a 20% and 60% reduction in root mean square error in the Kuroshio Extension and Gulf Stream regions, respectively. Additionally, it improved the effective resolution from 100 km to 20–30 km, enabling the dynamic tracking of mesoscale eddies. This advance provides a near-realtime solution for monitoring fine-scale ocean salinity processes, with practical implications for ocean dynamics research and the operational application of salinity satellite products.

Reconstruction of the daily three-dimensional (3D) subsurface salinity (SS) from satellite observations is essential for ocean 3D monitoring. Recent deep learning-based methods have mainly focused on monthly reconstruction and have limitations in processing multi-scale ocean dynamic processes. In this study, multivariate satellite observations are applied to reconstruct the daily 3D SS in the Pacific Ocean by the novel wavelet-enhanced 3D Mamba (3D-WaveMa) model. This model introduces the wavelet transform to spatially decompose multi-scale ocean dynamic processes, and efficiently captures the 3D long-range dependencies with linear complexity through the proposed 3D selective scanning module. Meanwhile, the K-means clustering guided transfer learning further enhances its ability to capture short-range dependencies in local regions. Model comparisons reveal that 3D-WaveMa outperforms the deep learning architectures commonly used for 3D SS reconstruction. The EN4 in situ and analysis products serve as the reference data for comparing 3D-WaveMa with mainstream ocean reanalysis and model products. The accuracy of the daily and monthly fields reconstructed by 3D-WaveMa is 4%–51% and 19%–58% higher, respectively. Case studies indicate that 3D-WaveMa enables the satellites to resolve the 3D structure of mesoscale eddies and their weekly changes, while capturing the structures and temporal changes of large-scale SS anomalies during a La Niña event. This study achieves daily 3D SS reconstruction, with resolution and accuracy comparable to ocean reanalysis and model products. It will contribute to satellite-based ocean 3D monitoring and the studies of ocean dynamic processes.

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data.

Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution of the salinity satellite SMOS from 1/4° and 10 days to 1/12° and daily. Leveraging Transformer, TSR captured long-range dependencies critical for reconstructing fine-scale structures. PTL effectively balanced structural detail acquisition and local accuracy correction by combining the gridded reanalysis products with scattered in situ observations as training labels. Validated against independent in situ measurements, TSR outperformed existing L3 salinity satellite products, as well as convolutional neural network and generative adversarial network-based SR models, particularly reducing the root mean square error (RMSE) by 33% and the mean bias (MB) by 81% compared to the SMOS input. More importantly, TSR demonstrated an enhanced capability in resolving mesoscale eddies, which were previously obscured by noise in salinity satellite products. Compared to training with a single label type or switching label types non-progressively, PTL achieved a 3%–66% lower RMSE and a 73–92% lower MB. TSR enables higher-resolution satellite monitoring of SSS, contributing to the study of ocean dynamics and climate change.