CaltechAUTHORS
Published April 2017 | Version public
Journal Article Metadata-only

Global land mapping of satellite-observed CO_2 total columns using spatio-temporal geostatistics

Creators

  • 1. ROR icon Chinese University of Hong Kong
  • 2. ROR icon Institute of Remote Sensing and Digital Earth
  • 3. ROR icon University of Toronto
  • 4. ROR icon University of Bremen
  • 5. ROR icon University of Wollongong
  • 6. ROR icon Los Alamos National Laboratory
  • 7. ROR icon Karlsruhe Institute of Technology
  • 8. ROR icon National Institute for Environmental Studies
  • 9. ROR icon Japan Aerospace Exploration Agency
  • 10. ROR icon California Institute of Technology

Abstract

This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO_2 total column (XCO_2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO2, and obtains global land maps of XCO_2, with a spatial grid resolution of 1° latitude by 1° longitude and temporal resolution of 3 days. We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways: (1) in cross-validation, the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations, (2) in comparison with ground truth provided by the Total Carbon Column Observing Network (TCCON), the predicted XCO_2 time series and those from TCCON sites are in good agreement, with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and (3) in comparison with model simulations, the spatio-temporal variability of XCO_2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO_2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Additional Information

© 2017 Taylor & Francis. Received 19 Oct 2015, Accepted 17 Feb 2016, Published online: 14 Apr 2016.

Additional details

Identifiers

Eprint ID
77382
Resolver ID
CaltechAUTHORS:20170511-153055961

Dates

Created
2017-05-12
Created from EPrint's datestamp field
Updated
2021-11-15
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Division of Geological and Planetary Sciences (GPS)