Estimating Ocean Subsurface Salinity from Remote Sensing Data by Machine Learning

Accurate estimation of ocean’s interior salinity information based on surface remote sensing data is quite significant for understanding complex dynamic processes in the ocean. This study adopts two kinds of ensemble learning algorithm, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) to estimate the subsurface salinity anomaly (SSA) in the upper 2000 m of the global ocean from multisource satellite-based sea surface parameters. The model performance is measured by R-square (R2) and normalized root-mean-square error (NRMSE). The results indicate the RF and GBDT models are both well suitable for retrieving SSA in the global ocean’s interior and RF model outperforms GBDT model; the models accuracy generally decreased with the depth below 500 m. This study is helpful in understanding subsurface and deeper ocean environment response to recent global warming.