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） is0.639°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.