Deriving Sea Subsurface Temperature Fields From Satellite Remote Sensing Data Using a Generative Adversarial Network Model

Accurately inverting global and regional subsurface temperature (ST) by multisource satellite observations is a challenging but hot topic. This study proposes a new method to invert daily ST from the sea surface information in China's marginal seas based on generative adversarial network (GAN) model. The proposed GAN-based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12 degrees. A traditional regression-based model, Modular Ocean Data Assimilation System (MODAS), is set up same experiments for comparison. The results show that the averaged root mean square error results are less than 1.45 degrees C in the upper 200 m and the highest averaged R-2 of 0.97 at the 70 m level, which is better than that of MODAS. Errors analysis and typical oceanographic phenomena analysis results show the superiority of the proposed GAN-based model in this study. This study can provide high-precision daily ST data from sea surface information, which can be expanded to further studies on the interior ocean variation characteristics.