Super-Resolution Reconstruction of SMOS Sea Surface Salinity from Multivariate Satellite Observations Based on Deep Learning

Abstract

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 SSS super-resolution reconstruction (S5R2) network. This deep learning framework achieved super-resolution (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. In addition, it improved the effective resolution from 100 km to 20–30 km, enabling the dynamic tracking of mesoscale eddies. This advance provides a near-real-time solution for monitoring fine-scale ocean salinity processes, with practical implications for ocean dynamics research and the operational application of salinity satellite products.