An Improved Method for Retrieving Subsurface Temperature Using the ConvLSTM Model in the Western Pacific Ocean

Abstract

In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time dependence and spatial correlation of marine characteristics, this study employed the convolutional long short-term memory (ConvLSTM) method to retrieve the subsurface temperature in the Western Pacific Ocean from several types of satellite observations. Furthermore, considering the temperature’s vertical distribution, the retrieved results for the upper layer were iteratively used in the calculation for the deeper layer as input data to improve the algorithm. The results show that the retrieved results for the 100 to 500 m depth temperature using the 50 m layer in the calculation resulted in higher accuracy than those retrieved from the standard ConvLSTM method. The largest improvement was in the calculation for the 100 m layer, where the thermocline was located. The results indicate that our improved ConvLSTM method can increase the accuracy of subsurface temperature retrieval without additional input data.

Keywords

• deep learning
• iterative optimization
• remote sensing data
• convolutional long short-term memory (ConvLSTM)
• marine temperature
• Western Pacific