Subsurface Temperature and Salinity Structures Inversion Using a Stacking-Based Fusion Model from Satellite Observations in the South China Sea

Three-dimensional ocean subsurface temperature and salinity structures (OST/OSS) in the South China Sea (SCS) play crucial roles in oceanic climate research and disaster mitigation. Traditionally, real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models, which are usually challenging and costly. Recently, dynamical, statistical, or machine learning models have been proposed to invert the OST/OSS from sea surface information; however, these models mainly focused on the inversion of monthly OST and OSS. To address this issue, we apply clustering algorithms and employ a stacking strategy to ensemble three models (XGBoost, Random Forest, and LightGBM) to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset. Subsequently, a fusion of temperature and salinity is employed to reconstruct OST and OSS. In the validation dataset, the depth-averaged Correlation (Corr) of the estimated OST (OSS) is 0.919 (0.83), and the average Root-Mean-Square Error (RMSE) is 0.639°C (0.087 psu), with a depth-averaged coefficient of determination (R²) of 0.84 (0.68). Notably, at the thermocline where the base models exhibit their maximum error, the stacking-based fusion model exhibited significant performance enhancement, with a maximum enhancement in OST and OSS inversion exceeding 10%. We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model (HYCOM) data and BOA_Argo dataset during the passage of a mesoscale eddy. This study shows that the proposed model can effectively invert the real-time OST and OSS, potentially enhancing the understanding of multi-scale oceanic processes in the SCS.