Global land 1° mapping dataset of XCO₂ from satellite observations of GOSAT and OCO-2 from 2009 to 2020

ABSTRACT

A global mapping data of atmospheric carbon dioxide (CO₂) concentrations can help us to better understand the spatiotemporal variations of CO₂ and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO₂, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO₂ (XCO₂) since 2009. However, these XCO₂ retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO₂ dataset (Mapping-XCO₂) using the XCO₂ retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO₂ values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO₂ that are revealed by the Mapping-XCO₂ are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO₂ dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO₂ and can support studies related to the carbon cycle and anthropogenic CO₂ emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

KEYWORDS:

• Global land mapping
• atmospheric CO₂ column concentration
• satellite observation
• GOSAT
• OCO-2

Acknowledgments

We are grateful for the GOSAT-ACOS v9r data and OCO-2 v10r data which are provided by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology and obtained from the ACOS/OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. We thank CarbonTracker CT2019B results provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. The TCCON data were obtained from the TCCON Data Archive hosted by CaltechDATA at https://tccondata.org.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary Material

Supplemental data for this article can be accessed here

Data Availability Statement

The dataset is openly available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2020YFA0607503), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19080303), and the Key Program of the Chinese Academy of Sciences (Grant No. ZDRW-ZS-2019-1-3).

Notes on contributors

Mengya Sheng

Mengya Sheng received the B.S. degrees in geographic information science from Zhengzhou University, Zhengzhou, China, in 2017. She is currently working toward the Ph.D. degree in cartography and geographic information system from the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China in 2017. Her current research interest is detecting the responses of atmospheric CO₂ concentrations to anthropogenic emissions and estimating regional anthropogenic emissions using satellite observations.

Liping Lei

Liping Lei is currently a Professor with the Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing. She received the B.A. and M.Sc. degrees from Peking University, Beijing, China, in 1983 and 1986, respectively, and the Ph.D. degree in computer and information science from the Graduate School of Engineering, Iwate University, Morioka, Japan, in 1999. Her current research interests are the application of remote sensing and the estimation of anthropogenic emissions using greenhouse gas satellite observations.

Zhao-Cheng Zeng

Zhao-Cheng Zeng is currently an Associate Research Scientist at Caltech. He obtained his PhD in 2016 from the Chinese University of Hong Kong. His research interests relate to the development of theories and algorithms in two fundamental areas of remote sensing: (1) radiative transfer and (2) inverse modeling/retrieval. More information about his research can be found at http://web.gps.caltech.edu/~zcz/.

Weiqiang Rao

Weiqiang Rao received the B.S. degrees in University of Electronic Science and Technology of China, Chengdu, China, in 2017. He is currently working toward the Ph.D. degree in Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China. His current research interest is hyperspectral image processing, especially anomaly and target detection algorithms based on deep learning.