Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) in China Seas (2003-2019)

This dataset is sea surface partial pressure of CO2 (pCO2) in China seas (0-42°N, 105-132°E) over 2003-2019 with a spatial resolution of 1km and temporal resolution of a month. This is our second version of pCO2 in China seas. The first version was published on the SatCO2 website (http://www.satco2.com/index.php?m=content&c=index&a=show&catid=317&id=188).

We produce this dataset by creating a boost machine learning algorithm (XGBoost) based on the gradient boost decision tree (GBDT). The input parameters used in this are sea surface temperature (SST), chlorophyll-a concentration (Chl-a), remote sensing reflectance of three bands (Rrs412, 443, 488 nm), the temperature difference in longitude direction (SST_DIF), and the theoretical background pCO2 (pCO2_therm) under corresponding SST. SST_DIF is derived from SST by subtracting the mean value at the same latitude. To calculate pCO2_therm, we first assume that the seawater background pCO2 equals the annual average atmospheric pCO2 (sea-air pCO2 balanced under ideal conditions), and the equilibrium temperature is assumed to be the yearly average SST; Then, the background pCO2 is corrected according to SST to obtain pCO2_therm. Air pressure at sea level (SLP) and mole fraction of CO2 in the air (xCO2) are employed when calculating atmospheric pCO2. 

The underway pCO2 is first gridded to monthly 1 km and then divided into the training and validation sets with the volumes 151009 and 32584 samples. The validation set shows coefficients of determination (R2) between the XGBoost model-predicted and in-situ pCO2 were 0.86, and the root means squared errors (RMSE) for the pCO2 were 21.1 μatm.