AUTHOR=Li Wei , Liu Chunli , Zhai Weidong , Liu Huizeng , Ma Wenjuan TITLE=Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea JOURNAL=Frontiers in Marine Science VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1181095 DOI=10.3389/fmars.2023.1181095 ISSN=2296-7745 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 (pCO₂^air) 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.