Subsurface thermohaline structure estimated by DO-ResNet model

Estimating ocean subsurface thermohaline information from satellite data is vital for understanding ocean dynamics and El Niño. This paper presents a double-output Residual Neural Network (DO-ResNet) to estimate ocean subsurface temperature (OST) and salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data. The model, trained and validated with Argo data, shows strong performance, with average RMSE and R² values of 0.34°C (0.05 psu) and 0.91 (0.95) for OST (OSS). DO-ResNet outperforms other models, effectively capturing spatial features and displaying strong seasonal adaptability. This research introduces a new AI method for estimating OST and OSS.

Variations in ocean mixed layer depth (MLD) show a significant impact on energy balance in the global climate systems and marine …