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.
['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 R. Henderson', 'R. Kivi', 'Rodica Lindenmaier', 'Isamu Morino', 'Justus Notholt', 'Hirofumi Ohyama', 'Christof Petri', 'Ralf Sussmann', 'V. Velazco', 'Paul O. Wennberg', 'Hui Lin']
Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics