Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO₂ (XCO₂), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO₂ concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO₂ from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO₂. The approach integrated XCO₂ from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO₂ (GM-XCO₂) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO₂ precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R² of 0.97 from cross-validation. GM-XCO₂ showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO₂ or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO₂ product may be also used in different carbon cycle research applications with different precision requirements.

[1]  Hartmut Boesch,et al.  Orbiting Carbon Observatory: Inverse method and prospective error analysis , 2008 .

[2]  Jovan M. Tadić,et al.  Spatio-temporal approach to moving window block kriging of satellite data v1.0 , 2016 .

[3]  David Crisp,et al.  Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X CO 2 measurements with TCCON , 2016 .

[4]  Jiancheng Shi,et al.  Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution , 2014, ATMOS 2014.

[5]  Tatsuya Yokota,et al.  Global Concentrations of CO2 and CH4 Retrieved from GOSAT: First Preliminary Results , 2009 .

[6]  Yawen Kong,et al.  Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research , 2019, Atmosphere.

[7]  David Crisp,et al.  Spaceborne detection of localized carbon dioxide sources , 2017, Science.

[8]  Luis Guanter,et al.  Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude , 2014, Nature.

[9]  E. A. Kort,et al.  Enhanced Seasonal Exchange of CO2 by Northern Ecosystems Since 1960 , 2013, Science.

[10]  Phaedon C. Kyriakidis,et al.  Geostatistical Space–Time Models: A Review , 1999 .

[11]  D. Myers,et al.  Space–time analysis using a general product–sum model , 2001 .

[12]  B. Connor,et al.  Intercomparison of remote sounding instruments , 1999 .

[13]  Hartmut Boesch,et al.  Does GOSAT capture the true seasonal cycle of carbon dioxide , 2015 .

[14]  Tatsuya Yokota,et al.  Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data , 2013 .

[15]  Shamil Maksyutov,et al.  Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations , 2014 .

[16]  Rebecca Castano,et al.  A method for evaluating bias in global measurements of CO 2 total columns from space , 2011 .

[17]  Liangyun Liu,et al.  Detection of Spatiotemporal Extreme Changes in Atmospheric CO2 Concentration Based on Satellite Observations , 2018, Remote. Sens..

[18]  Dell,et al.  Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño , 2017, Science.

[19]  Maximilian Reuter,et al.  Anthropogenic carbon dioxide source areas observed from space: assessment of regional enhancements and trends , 2012 .

[20]  S. Wofsy,et al.  Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region , 2012, Proceedings of the National Academy of Sciences.

[21]  R. DeFries,et al.  Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system , 2013 .

[22]  Jiancheng Shi,et al.  Combining XCO2 Measurements Derived from SCIAMACHY and GOSAT for Potentially Generating Global CO2 Maps with High Spatiotemporal Resolution , 2014, PloS one.

[23]  Ying Sun,et al.  The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes , 2017, Science.

[24]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[25]  R. Parker,et al.  Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: sensitivity to measurement bias inside and outside Europe , 2016 .

[26]  J. Tamminen,et al.  Direct space‐based observations of anthropogenic CO2 emission areas from OCO‐2 , 2016 .

[27]  John Robinson,et al.  Retrieval of atmospheric CO2 with enhanced accuracy and precision from SCIAMACHY: validation with FTS measurements and comparison with model results , 2011 .

[28]  Hartmut Boesch,et al.  Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): Comparison with ground‐based TCCON observations and GEOS‐Chem model calculations , 2012 .

[29]  Hartmut Boesch,et al.  Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission , 2011, Remote. Sens..

[30]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations , 2011 .

[31]  J. Randerson,et al.  An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker , 2007, Proceedings of the National Academy of Sciences.

[32]  Akihiko Kuze,et al.  Consistent satellite XCO 2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm , 2015 .

[33]  Atul K. Jain,et al.  Global Carbon Budget 2015 , 2015 .

[34]  David Crisp,et al.  The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2 , 2017, Remote. Sens..

[35]  Hui Lin,et al.  Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics , 2017, Int. J. Digit. Earth.

[36]  Taro Takahashi,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[37]  Luis Guanter,et al.  Anomalous carbon uptake in Australia as seen by GOSAT , 2015 .

[38]  James T. Randerson,et al.  Differences between surface and column atmospheric CO2 and implications for carbon cycle research , 2004 .

[39]  Hartmut Boesch,et al.  Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2 , 2014 .

[40]  M. Buchwitz,et al.  SCIAMACHY: Mission Objectives and Measurement Modes , 1999 .

[41]  Christopher B. Field,et al.  The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide , 1997 .

[42]  Martin Heimann,et al.  Searching out the sinks , 2009 .

[43]  Noel Cressie,et al.  Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets , 2014, Technometrics.

[44]  Tatsuya Yokota,et al.  Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method , 2015 .

[45]  Sandra De Iaco,et al.  Space–time correlation analysis: a comparative study , 2010 .

[46]  Min Liu,et al.  Geostatistical Analysis of CH4 Columns over Monsoon Asia Using Five Years of GOSAT Observations , 2016, Remote. Sens..

[47]  David Crisp,et al.  Quantifying CO2 Emissions From Individual Power Plants From Space , 2017 .

[48]  F. Joos,et al.  Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years , 2008, Proceedings of the National Academy of Sciences.

[49]  Hiroshi Tani,et al.  Mapping the FTS SWIR L2 product of XCO2 and XCH4 data from the GOSAT by the Kriging method – a case study in East Asia , 2012 .

[50]  Bing Zhang,et al.  A Regional Gap-Filling Method Based on Spatiotemporal Variogram Model of $\hbox{CO}_{2}$ Columns , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Wei Gong,et al.  Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON , 2017, Remote. Sens..

[52]  Pauli Heikkinen,et al.  Inferring regional sources and sinks of atmospheric CO 2 from GOSAT XCO 2 data , 2013 .

[53]  Shaoyuan Yang,et al.  A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations , 2017, Remote. Sens..

[54]  Dorit Hammerling,et al.  Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT) , 2012 .