Spatiotemporal Distribution of High Coverage XCO2 Reconstructed from OCO-2 Satellite Data in China
Satellite remote sensing is one of the important methods to understand the spatiotemporal distribution of atmospheric carbon dioxide(CO2).However,due to the restricts of detection technology,the satellite retrieved column-averaged mole fraction of carbon dioxide(XCO2)contains a large amount of missing data,which is insufficient to reflect the full spatiotemporal distribution of carbon dioxide concentrations.In this study,based on satellite(OCO-2,TROPOMI)and model(Carbon Tracker,ERA5-Reanalysis)data,we used a time series fitting model and a random forest model to reconstruct the XCO2 with a high spatial resolution(0.05°×0.05°)for China during the period from 2019 to 2022.Compared with OCO-2 and Carbon Tracker XCO2,the reconstructed XCO2 was better consistent with OCO-2 observations,with a root mean square error(RMSE)of 1.05×10-6 and a high correlation coefficient(R2)of 0.96.Based on the reconstructed XCO2,it was found that XCO2 shows significant seasonal fluctuations,with higher values in winter and spring and lower values in summer and autumn.From 2019 to 2022,XCO2 in China showed an increasing trend with a growth rate of(2.41±0.01)× 10-6/a,but the growth rate has slowed down in recent years.In terms of spatial distribution,XCO2 in eastern,northern,and central China is significantly higher than other regions,so as the growth rate.Among above regions,Hangzhou,Tianjin,and Chengdu have the fastest XCO2 growth rates.The research findings of this study provide data basis for carbon monitoring research,carbon emission inventory verification,carbon emission management,greenhouse gas reduction,and other related studies.
Column-averaged mole fraction of carbon dioxide(XCO2)ReconstructionSpatiotemporal distributionRandom forest
万方数据
