Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics
This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the lati...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-04-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2016.1156777 |
| _version_ | 1797678640610148352 |
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| author | Zhao-Cheng Zeng Liping Lei Kimberly Strong Dylan B. A. Jones Lijie Guo Min Liu Feng Deng Nicholas M. Deutscher Manvendra K. Dubey David W. T. Griffith Frank Hase Bradley Henderson Rigel Kivi Rodica Lindenmaier Isamu Morino Justus Notholt Hirofumi Ohyama Christof Petri Ralf Sussmann Voltaire A. Velazco Paul O. Wennberg Hui Lin |
| author_facet | Zhao-Cheng Zeng Liping Lei Kimberly Strong Dylan B. A. Jones Lijie Guo Min Liu Feng Deng Nicholas M. Deutscher Manvendra K. Dubey David W. T. Griffith Frank Hase Bradley Henderson Rigel Kivi Rodica Lindenmaier Isamu Morino Justus Notholt Hirofumi Ohyama Christof Petri Ralf Sussmann Voltaire A. Velazco Paul O. Wennberg Hui Lin |
| author_sort | Zhao-Cheng Zeng |
| collection | DOAJ |
| description | This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) 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 XCO2, 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 XCO2 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 XCO2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming. |
| first_indexed | 2024-03-11T23:02:52Z |
| format | Article |
| id | doaj.art-ab9c35932419458d87efc856d069ac4c |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:02:52Z |
| publishDate | 2017-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-ab9c35932419458d87efc856d069ac4c2023-09-21T14:38:04ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552017-04-0110442645610.1080/17538947.2016.11567771156777Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatisticsZhao-Cheng Zeng0Liping Lei1Kimberly Strong2Dylan B. A. Jones3Lijie Guo4Min Liu5Feng Deng6Nicholas M. Deutscher7Manvendra K. Dubey8David W. T. Griffith9Frank Hase10Bradley Henderson11Rigel Kivi12Rodica Lindenmaier13Isamu Morino14Justus Notholt15Hirofumi Ohyama16Christof Petri17Ralf Sussmann18Voltaire A. Velazco19Paul O. Wennberg20Hui Lin21Institute of Space and Earth Information Science, The Chinese University of Hong KongInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUniversity of TorontoUniversity of TorontoInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesInstitute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUniversity of TorontoUniversity of BremenLos Alamos National LaboratoryUniversity of WollongongKarlsruhe Institute of Technology, Institute for Meteorology and Climate Research (IMK-ASF)Los Alamos National LaboratoryFMI Arctic Research CenterLos Alamos National LaboratoryNational Institute for Environmental Studies (NIES)University of BremenEarth Observation Research Center, Japan Aerospace Exploration Agency (JAXA)University of BremenKarlsruhe Institute of Technology, IMK-IFUUniversity of WollongongCalifornia Institute of TechnologyInstitute of Space and Earth Information Science, The Chinese University of Hong KongThis study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) 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 XCO2, 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 XCO2 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 XCO2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.http://dx.doi.org/10.1080/17538947.2016.1156777xco2acos-gosatspatio-temporal geostatisticsglobal mappinggeospatial dataset |
| spellingShingle | Zhao-Cheng Zeng Liping Lei Kimberly Strong Dylan B. A. Jones Lijie Guo Min Liu Feng Deng Nicholas M. Deutscher Manvendra K. Dubey David W. T. Griffith Frank Hase Bradley Henderson Rigel Kivi Rodica Lindenmaier Isamu Morino Justus Notholt Hirofumi Ohyama Christof Petri Ralf Sussmann Voltaire A. Velazco Paul O. Wennberg Hui Lin Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics International Journal of Digital Earth xco2 acos-gosat spatio-temporal geostatistics global mapping geospatial dataset |
| title | Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics |
| title_full | Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics |
| title_fullStr | Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics |
| title_full_unstemmed | Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics |
| title_short | Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics |
| title_sort | global land mapping of satellite observed co2 total columns using spatio temporal geostatistics |
| topic | xco2 acos-gosat spatio-temporal geostatistics global mapping geospatial dataset |
| url | http://dx.doi.org/10.1080/17538947.2016.1156777 |
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