Satellite estimation of global air sea CO2 flux from 2000 to 2020

The global ocean carbon dioxide flux has shown a slow upward trend. Based on more than 160,000 quality-controlled measurements of surface ocean carbon dioxide fugacity from 2000 to 2020, a satellite-based ocean–atmosphere carbon dioxide fugacity (fCO2) retrieval algorithm was developed using machine learning methods. A comparative analysis was conducted among various machine learning methods, including XGBoost, random forest, light gradient boosting machine, feedforward neural network, convolutional neural network, and backpropagation neural network. Based on the best performance, the random forest algorithm was selected for model construction. The results of independent on-site validation indicated that the model had a low root mean square error (RMSE = 14.35μatm), mean absolute percentage error (MAPE = 2.61%), and coefficient of determination (R2 = 0.86). The distribution of global air-sea carbon dioxide fugacity from 2000 to 2020 was reconstructed at a resolution of 0.25°×0.25°, and the air–sea carbon dioxide flux (FCO2) of the global ocean surface during the period of 2000–2020 was further estimated at a resolution of 0.25°×0.25°. During the period of 2000–2020, the global ocean CO2 uptake increased from 1.443 PgC/year in 2000 to 1.894 PgC/year in 2020, and the air-sea carbon dioxide flux in the entire study area increased by 31.2% over the 20 years. These complete oceanic carbon sink statistics and new insights will help further research on carbon fixation and its potential capacity.