Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea

Abstract

With global climate changing, the carbon dioxide (CO2) absorption rates increased in marginal seas. Due to the limited availability of in-situ spatial and temporal distribution data, the current status of the sea surface carbon dioxide partial pressure (pCO2) in the Yellow Sea is unclear. Therefore, a pCO2 model based on a random forest algorithm has been developed, which was trained and tested using 14 cruise data sets from 2011 to 2019, and remote sensing satellite sea surface temperature, chlorophyll concentration, diffuse attenuation of downwelling irradiance, and in-situ salinity were used as the input variables. The seasonal and interannual variations of modeled pCO2 were discussed from January 2003 and December 2021 in the Yellow Sea. The results showed that the model developed for this study performed well, with a root mean square difference (RMSD) of 43 μatm and a coefficient of determination (R2) of 0.67. Moreover, modeled pCO2 increased at a rate of 0.36 μatm year-1 (R2 = 0.27, p < 0.05) in the YS, which is much slower than the rate of atmospheric pCO2 (pCO2air) rise. The reason behind it needs further investigation. Compared with pCO2 from other datasets, the pCO2 derived from the RF model exhibited greater consistency with the in-situ pCO2 (RMSD = 55 μatm). In general, the RF model has significant improvement over the previous models and the global data sets.