EOF-UViT Model: A New Deep Learning Model to Reconstruct the Three-Dimensional Salinity Based on Multi-Source Remote Sensing Data

Accurate three-dimensional (3D) salinity fields are crucial for diagnosing freshwater transport and upper-to-intermediate ocean dynamics, yet subsurface salinity observations remain uneven in space and time, especially away from primary observing corridors. Gridded fields derived from sparse measurements can also suffer from regional biases and over-smoothing, which may blur mesoscale signals in energetic regimes. This study presents an empirical orthogonal function-guided U-shaped Vision Transformer (EOF-UViT) to reconstruct daily 3D salinity in the Northwest Pacific (0–50°N, 100–150°E) from multi-source surface remote-sensing factors. Compared with the U-Net baseline and the Modular Ocean Data Assimilation System (MODAS) reconstruction, EOF-UViT produces more realistic horizontal structures and improved vertical consistency, with the largest gains in dynamically active regions. Comparison with collocated in situ profiles further supports the reconstruction skill (RMSE = 0.094 psu; R2 = 0.904), with estimates clustering more tightly around the 1:1 line than MODAS. Overall, EOF-UViT provides an efficient, observation-driven route to spatially coherent 3D salinity fields, supporting applications such as model initialization, assimilation background fields, and basin-scale salinity variability analysis.