Salinity Inversion of Flat Sea Surface Based on Deep Neural Network

Abstract

The distribution and change of sea surface salinity (SSS) have an important influence on the sea dynamic environment, marine ecological environment, global water cycle, and global climate change. Satellite remote sensing is the only practical way to continuously observe SSS over a wide area and for a long period of time. The salinity retrieval model of flat sea surface, which primarily includes empirical model and iterative model, is the key to retrieving satellite SSS products. The empirical models have high computational efficiency but low inversion accuracy, while the iterative models have high inversion accuracy but low computational efficiency. In order to reconcile the contradiction between the computational efficiency and inversion accuracy of existing models, this paper proposes a universal deep neural network (DNN) model architecture and corresponding training scheme, and provides 3 DNN models with extremely high computational efficiency and high inversion accuracy. The inversion error range, the root mean square error (RMSE), and the mean absolute error (MAE) of the DNN models on 311,121 sets of data have decreased by more than 40 times, 150 times, and 150 times, respectively, compared to the empirical model. The computational efficiency of the DNN models on 420,903 sets of data has improved by more than 100,000 times compared to the iterative model. Therefore, the algorithm developed in this paper can effectively solve the contradiction between the computational efficiency and inversion accuracy of existing models, and provide a theoretical support for high-precision and high-efficiency salinity inversion research.