Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 31;19(17):10853.
doi: 10.3390/ijerph191710853.

High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region

Wei Wang  1 , Junchen He  1 , Huihui Feng  1 , Zhili Jin  1
Affiliations

High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region

Wei Wang et al. Int J Environ Res Public Health. .

Abstract

The extreme climate caused by global warming has had a great impact on the earth's ecology. As the main greenhouse gas, atmospheric CO2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO2 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 CO2 and various factors affecting the spatial distribution of CO2, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO2 column concentration (XCO2) 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 (R2) = 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 R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO2 concentrations from 2015 to 2019 in the Beijing-Tianjin-Hebei region was conducted using the established model. The study of the spatial distribution of XCO2 concentration in the Beijing-Tianjin-Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO2 map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Keywords: CO2; mapping; random forest; remote sensing; satellite.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Land use types in the Beijing–Tianjin–Hebei (BTH) region in China.
Figure 2
Figure 2
Average monthly data of OCO-2 XCO L2 Lite_FP in China: (a) January 2015, (b) January 2016, (c) January 2017, and (d) January 2018.
Figure 3
Figure 3
Regional mean value results of VIIRS S-NPP luminous data. The left and right figures show the regional mean value results of luminous data on (a) 1 January 2015 and (b) 1 January 2016, respectively.
Figure 4
Figure 4
Flow chart of this study.
Figure 5
Figure 5
The frequency histogram of parameters in XCO2 concentration modeling (n = 62,964). (a–i) represent CO2 column concentration, digital number, temperature, relative humidity, pressure, vertical wind speed, horizontal wind speed, boundary layer height, and normalized vegetation index, respectively.
Figure 6
Figure 6
Scatter density plot of (a) direct fitting, (b) sample-based cross-validation, and (c) spatial cross-validation.
Figure 7
Figure 7
Seasonal means of OCO-2 XCO2 L2 Lite_FP data during 20150301–20190228, all resampled to 0.05° × 0.05° spatial resolution: (a–d) 2015, (e–h) 2016, (i–l) 2017, and (m–p) 2018 in spring, summer, autumn, and winter, respectively.
Figure 8
Figure 8
Seasonal XCO2 in the Beijing–Tianjin–Hebei region estimated by the random forest model from March 1, 2015 to February 28, 2019: (a–d) 2015, (e–h) 2016, (i–l) 2017, and (m–p) 2018 in spring, summer, autumn, and winter, respectively.
Figure 9
Figure 9
Comparison between monthly XCO2 concentrations from the OCO-2 satellite (red line) and the random forest model (blue line), as well as the deviation value (green line), where the deviation value increased by 400 ppm (yellow dotted line).
Figure 10
Figure 10
Monthly average XCO2 concentrations in the Beijing–Tianjin–Hebei region from January 2015 to December 2015. ZJK, CD, BJ, TJ, TS, and SJZ represent Zhangjiakou, Chengde, Beijing, Tianjin, Tangshan, and Shijiazhuang, respectively. (a–l) represent January to December respectively.
Figure 11
Figure 11
Monthly average XCO2 concentrations in the Beijing–Tianjin–Hebei region from January 2016 to December 2016. ZJK, CD, BJ, TJ, TS, and SJZ represent Zhangjiakou, Chengde, Beijing, Tianjin, Tangshan, and Shijiazhuang, respectively. (a–l) represent January to December respectively.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Friedlingstein P., O’Sullivan M., Jones M.W., Andrew R.M., Zaehle S. Global Carbon Budget 2020. Glob. Carbon Budg. 2020;12:3269–3340. doi: 10.5194/essd-12-3269-2020. - DOI
    1. Kappelle M. WMO Statement on the State of the Global Climate in 2019. World Meteorological Organization; Geneva, Switzerland: 2020.
    1. Pachauri R.K., Meyer L., Groups I.T. IPCC, Climate Change 2014: Synthesis Report. IPCC; Geneva, Switzerland: 2014. 151p
    1. Dietz S., Bowen A., Dixon C., Gradwell P. ‘Climate value at risk’ of global financial assets. Nat. Clim. Change. 2016;6:676–679. doi: 10.1038/nclimate2972. - DOI
    1. Gurney K.R., Law R.M., Denning A.S., Rayner P.J., Baker D., Bousquet P., Bruhwiler L., Chen Y.H., Ciais P., Fan S., et al. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature. 2002;415:626–630. doi: 10.1038/415626a. - DOI - PubMed

Publication types

Grants and funding

This study was supported by the National Natural Science Foundation of China (42071378 and 41901295), the Basic Science-Center Project of National Natural Science Foundation of China (72088101), the Natural Science Foundation of Hunan Province, China (2020JJ5708), and the Key Program of the National Natural Science Foundation of China (41930108).