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

Abstract

Ingenious use of multisource satellite observations to accurately invert global and regional subsurface thermohaline structure is essential for understanding ocean interior processes, but extremely challenging. This study proposes a new method from the sea surface information inverting daily subsurface temperature (ST) based on generative adversarial network (GAN) model in China's marginal seas. The proposed GAN-based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12°. A traditional regression-based model, Modular Ocean Data Assimilation System (MODAS), is set up the same experiments for the sake of comparison. The results show that the averaged RMSE results are less than 1.45°C in upper 200m and the highest averaged R2 of 0.97 at the 70m level, which are 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 ocean interior variation characteristics.

Global Seawater Density Distribution Model Using Convolutional Neural Network

“…In recent years, oceanographic researchers have employed deep learning to develop seawater temperature, salinity, and tide prediction models [18][19][20], achieving some success. This in turn provides a sound basis for establishing seawater density within the scope of this paper.…”