Transformer-Based Model Sea Temperature and Salinity Prediction Using Satellite Remote Sensing and Argo Data Fusion

Due to the intensification of global warming, changes in sea temperature and salinity have profound impacts on ecosystems and climate. Accurately predicting marine environmental changes has thus become an important issue. However, the spatial limitations inherent in Argo floats and satellite remote sensing data pose challenges to prediction. This study integrates data collected from Argo floats and satellite remote sensing, employing a Transformer-based model to predict sea temperature and salinity at different times, latitudes, and depths below 50 meters beneath the sea surface, aiming to supplement the shortcomings of Argo floats data and satellite remote sensing data and achieve comprehensive marine environmental prediction. The results of the study demonstrate that the proposed Transformer-based model performs well in sea temperature prediction, with a mean absolute error (MAE) of 0.489°C, root mean square error (RMSE) of 0.809°C, and the coefficient of determination (R2) of 0.983. Regarding salinity prediction, the model also exhibits excellent performance, with MAE of 0.167 psu, RMSE of 0.229 psu, and R2 of 0.805. Compared to other models, the model proposed in this study shows superior performance in predicting sea temperature and salinity, providing an important tool and reference for future marine resource management and climate change research.