Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Harmful algal blooms (HABs) events have a serious impact on marine fisheries and marine management. They occur globally with high frequency and are characterized by a long duration and difficult governance. HABs incidents have occurred in the South China Sea (SCS), and the frequency of occurrence has been on the rise in recent decades. Predicting the long-term chlorophyll-a (Chl-a) concentration has the potential to facilitate long-term monitoring and early warning of HABs events. Currently, long-term predictions of ocean circulation and temperature are common, while long-term predictions of marine biochemistry are still in their infancy. Traditional Chl-a prediction methods have problems, such as low accuracy and the inability to carry out long-term predictions. This research improved the CNN-LSTM model by combining spatio-temporal features to predict Chl-a concentrations. This model can extract both the temporal and spatial features of Chl-a, expand the dataset, and improve the prediction accuracy and training speed. The predictions were made using a Chl-a dataset for the Reed Tablemount in the SCS. The time series of Chl-a used was the satellite data of NASA's official website from January 2002 to June 2020. The results indicate that the predictions of the CNN-LSTM model are better than those of the LSTM and SARIMA models. The five-year long-term rolling prediction of Chl-a was carried out, and the three-year Pearson correlation coefficient reached 0.5. The novelty of this study is the realization of a three-year long-term prediction of Chl-a concentrations. The Mann-Kendall trend test method and the least square method were used to fit the straight line to detect the trend of the five-year predicted value and the true value, respectively. The results indicated that the prediction value and true value of the sea surface Chl-a from 2015 to 2020 both exhibited an overall upward trend. In addition, the prediction performance of the model in large-scale prediction is better than that in small-scale prediction.