High-Coverage Reconstruction of XCO 2 Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region

Wei Wang, Junchen He, Huihui Feng () and Zhili Jin
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Wei Wang: School of Geosciences and Info-Physics, Central South University, Changsha 410017, China
Junchen He: School of Geosciences and Info-Physics, Central South University, Changsha 410017, China
Huihui Feng: School of Geosciences and Info-Physics, Central South University, Changsha 410017, China
Zhili Jin: School of Geosciences and Info-Physics, Central South University, Changsha 410017, China

IJERPH, 2022, vol. 19, issue 17, 1-20

Abstract: The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO 2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO 2 concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO 2 and various factors affecting the spatial distribution of CO 2 , this study used multisource satellite-based data and a random forest model to reconstruct the daily CO 2 column concentration (XCO 2 ) with full spatial coverage in the Beijing–Tianjin–Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R 2 ) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R 2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R 2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO 2 concentrations from 2015 to 2019 in the Beijing–Tianjin–Hebei region was conducted using the established model. The study of the spatial distribution of XCO 2 concentration in the Beijing–Tianjin–Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO 2 map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Keywords: satellite; remote sensing; CO 2; mapping; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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