Mid-Long-Term Prediction of Surface Seawater Organic Carbon in the Southern South China Sea Based on Multi-Applicability CNN-LSTM Prediction Model

Abstract

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon.