An Information Spatial-Temporal Extension Algorithm for Shipborne Predictions Based on Deep Neural Networks with Remote Sensing Observations—Part I: Ocean Temperature

Abstract

For ships on voyage, using satellite remote sensing observations is an effective way to access ocean temperature. However, satellite remote sensing observations can only provide the surface information. Additionally, this information obtained from satellite remote sensing observations is delayed data. Although some previous studies have investigated the spatial inversion (spatial extension) or temporal prediction (temporal extension) of satellite remote sensing observations, these studies did not integrate ship survey observations and the temporal prediction is limited to sea surface temperature (SST). To address these issues, we propose an information spatial-temporal extension (ISTE) algorithm for remote sensing SST. Based on deep neural networks (DNNs), the ISTE algorithm can effectively fuse the satellite remote sensing SST data, ship survey observations data, and historical data to generate a four-dimensional (4D) temperature prediction field. Experimental results show that the ISTE algorithm performs superior prediction accuracy relative to linear regression analysis-based prediction. The prediction results of ISTE exhibit high coefficient of determination (0.9936) and low root mean squared errors (around 0.7 °C) compared with Argo observation data. Therefore, for shipborne predictions, the ISTE algorithm driven by satellite remote sensing SST can be as an effective approach to predict ocean temperature.