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 an...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 XCO_(2),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.展开更多
基金Work at the Chinese University of Hong Kong(CUHK)was supported by the Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences(CAS-RADI,No.2014LDE010)National Key Basic Research Program of China(2015CB954103)+2 种基金Work at the RADI-CAS was funded by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues of the Chinese Academy of Sciences(No.XDA05040401)Work at University of Toronto is supported by the global scholarship program for research excellent from CUHK to Z.-C.ZengThe TCCON Network is supported by NASA’s Carbon Cycle Science Program through a grant to the California Institute of Technology.TCCON data were obtained from the TCCON Data Archive,operated by the California Institute of Technology from the website at http://tccon.ipac.caltech.edu/.Measurement programs at Darwin and Wollongong are supported by the Australian Research Council under grants DP140101552,DP110103118,DP0879468352,LP0562346.A part of work for Saga site at JAXA was supported by the Environment Research and Technology Development Fund(A-1102)of the Ministry of the Environment,Japan.Four Corners TCCON site was funded by LANL’s LDRD Project(20110081DR).
文摘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 XCO_(2),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.
