Remote Sensing Estimations of the Seawater Partial Pressure of CO2 Using Sea Surface Roughness Derived From Synthetic Aperture Radar

Remote sensing study of the carbon cycle in coastal marine systems using machine learning methods has received significant attention recently. The partial pressure of carbon dioxide (CO2) in seawater ( pCO(2w)) is a crucial parameter for quantifying the air-sea carbon dioxide exchange. However, previous studies did not consider the effect of sea surface roughness (SSR) on pCO(2w) caused by wind, waves, and other ocean dynamics. In this study, for the first time, we used SSR data derived from synthetic aperture radar (SAR), with sea surface temperature (SST), chlorophyll-a (Chl-a) concentration, sea surface salinity (SSS) conventional remote sensing data to predict the pCO(2w) data along the North American East Coast from 2015 to 2021 using the Cubist algorithm. Results show that the semi-analytic algorithm, Cubist, performs best among 20 statistical and machine learning models. Moreover, compared with the control experiment without the SSR data, after adding SSR as an independent variable, the final Cubist model's coefficient of determination ( R-2 ) increased from 0.88 to 0.95, and the root mean square error (RMSE) reduced from 21.75 to 14.79 mu atm. Our results showed significant improvement over the previous study ( R-2 = 0.8), proving the applicability of applying SSR data in retrieving high spatial resolution carbonate system parameters in the future, especially for coastal regions where wind and wave dynamics are more variable.