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

Zeng, Zhao-Cheng and Lei, Liping and Strong, Kimberly and Jones, Dylan B. A. and Guo, Lijie and Liu, Min and Deng, Feng and Deutscher, Nicholas M. and Dubey, Manvendra K. and Griffith, David W. T. and Hase, Frank and Henderson, Bradley and Kivi, Rigel and Lindenmaier, Rodica and Morino, Isamu and Notholt, Justus and Ohyama, Hirofumi and Petri, Christof and Sussmann, Ralf and Velazco, Voltaire A. and Wennberg, Paul O. and Lin, Hui (2017) Global land mapping of satellite-observed CO_2 total columns using spatio-temporal geostatistics. International Journal of Digital Earth, 10 (4). pp. 426-456. ISSN 1753-8947. doi:10.1080/17538947.2016.1156777.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Zeng, Zhao-Cheng0000-0002-0008-6508
Deng, Feng0000-0002-1381-0243
Deutscher, Nicholas M.0000-0002-2906-2577
Griffith, David W. T.0000-0002-7986-1924
Morino, Isamu0000-0003-2720-1569
Petri, Christof0000-0002-7010-5532
Velazco, Voltaire A.0000-0002-1376-438X
Wennberg, Paul O.0000-0002-6126-3854
Additional Information:© 2017 Taylor & Francis. Received 19 Oct 2015, Accepted 17 Feb 2016, Published online: 14 Apr 2016.
Subject Keywords:XCO_2, ACOS-GOSAT, Spatio-temporal geostatistics, global mapping, geospatial dataset
Issue or Number:4
Record Number:CaltechAUTHORS:20170511-153055961
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:77382
Deposited By: Tony Diaz
Deposited On:12 May 2017 23:24
Last Modified:15 Nov 2021 17:30

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