A Spatiotemporal Fusion Transformer Model for Chlorophyll-a Concentrations Prediction Over Large Areas With Satellite Time Series Data

Predicting Chlorophyll-a (Chla) is essential to support the marine environment changes and marine ecosystem health, and provide early warning of algae blooms. The development of learning-based methods has facilitated Chla prediction research. Still, most of the current methods can only predict short-term Chla changes in small areas, which is limited by the ability of the model to exploit spatiotemporal dependencies. Thus, this article proposes a spatiotemporal fusion transformer prediction model (STF_Transformer) to predict relatively long-term Chla changes (15 days ahead). This model utilizes temporal and spatial transformer modules to extract the temporal and spatial correlations of the input spatiotemporal sequences, which are then fused to predict the 15-day Chla. The experimental results show that the proposed model has the optimal performance compared to the existing methods [e.g., convolutional neural network (CNN), long- and short-term memory (LSTM), and convolutional LSTM (ConvLSTM)], with root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) less than 0.61 mg/m3, 0.315 mg/m3, and 22.5%, respectively, for 15 days Chla prediction in a large area (including Bohai, Yellow, and East China Sea). In addition, the temporal and spatial prediction results of the proposed model show that the predicted Chla has consistent temporal and spatial patterns with the observed Chla. This study indicates that the proposed STF_Transformer model can provide a highly accurate prediction of Chla over a large area in the relatively long term (15 days), providing data and technical support for marine ecosystem-related applications.