Capturing the net ecosystem CO2 exchange dynamics of tidal wetlands with high spatiotemporal resolution by integrating process-based and machine learning estimations

Accurate estimation of the net ecosystem CO2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora, and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 mu mol m- 2 s- 1, 8.105 mu mol m- 2 s- 1, and 0.109 mu mol m- 2 s- 1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be -0.297 and -0.444 Tg C yr-1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales.