Daily Subsurface Salinity Reconstruction From Multisource Satellite Observations Using Wavelet-Enhanced 3-D Mamba

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.