Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets

Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual evolutions. Currently, two main remote sensing instruments for total-column CO2 are the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse gases Observing SATellite (GOSAT), both of which produce estimates of CO2 concentration, called profiles, at 20 different pressure levels. Operationally, each profile estimate is then convolved into a single estimate of column-averaged CO2 using a linear pressure weighting function. This total-column CO2 is then used for subsequent analyses such as Level 3 map generation and colocation for validation. In principle, total-column CO2 in these applications may be more efficiently estimated by making optimal estimates of the vector-valued CO2 profiles and applying the pressure weighting function afterwards. These estimates will be more efficient if there is multivariate dependence between CO2 values in the profile. In this article, we describe a methodology that uses a modified Spatial Random Effects model to account for the multivariate nature of the data fusion of OCO-2 and GOSAT. We show that multivariate fusion of the profiles has improved mean squared error relative to scalar fusion of the column-averaged CO2 values from OCO-2 and GOSAT. The computations scale linearly with the number of data points, making it suitable for the typically massive remote sensing datasets. Furthermore, the methodology properly accounts for differences in instrument footprint, measurement-error characteristics, and data coverages.

[1]  L. Mark Berliner,et al.  Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds , 2001 .

[2]  K. Moffett,et al.  Remote Sens , 2015 .

[3]  D. Wunch,et al.  Total column CO 2 measurements at Darwin, Australia – site description and calibration against in situ aircraft profiles , 2010 .

[4]  Akihiko Kuze,et al.  Fourier transform spectrometer for Greenhouse Gases Observing Satellite (GOSAT) , 2005, SPIE Asia-Pacific Remote Sensing.

[5]  Noel A Cressie,et al.  Change of support and the modifiable areal unit problem , 1996 .

[6]  Mikyoung Jun,et al.  Nonstationary covariance models for global data , 2008, 0901.3980.

[7]  M. Noguer,et al.  Climate change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change , 2002 .

[8]  Mevin B. Hooten,et al.  Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model , 2003, Landscape Ecology.

[9]  N. Cressie,et al.  Spatial Statistical Data Fusion for Remote Sensing Applications , 2012 .

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

[11]  Matthias Katzfuss,et al.  Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets , 2011 .

[12]  G. Toon,et al.  Carbon dioxide column abundances at the Wisconsin Tall Tower site , 2006 .

[13]  Christopher K. Wikle,et al.  Multiresolution Wavelet Analyses in Hierarchical Bayesian Turbulence Models , 1999 .

[14]  J. Andrew Royle,et al.  Multiresolution models for nonstationary spatial covariance functions , 2002 .

[15]  J. Andrew Royle,et al.  Efficient statistical mapping of avian count data , 2005, Environmental and Ecological Statistics.

[16]  Taro Takahashi,et al.  Oceanic sources, sinks, and transport of atmospheric CO2 , 2009 .

[17]  S. R. Searle,et al.  On Deriving the Inverse of a Sum of Matrices , 1981 .

[18]  Philippe Bousquet,et al.  Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data , 2005 .

[19]  N. Cressie,et al.  Fixed Rank Filtering for Spatio-Temporal Data , 2010 .

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

[21]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[22]  Ann Henderson-Sellers,et al.  A Climate Modelling Primer , 1987 .

[23]  Catherine A. Calder,et al.  A dynamic process convolution approach to modeling ambient particulate matter concentrations , 2008 .

[24]  Tatsuya Yokota,et al.  Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra , 2010 .

[25]  James B. Abshire,et al.  Calibration of the Total Carbon Column Observing Network using aircraft profile data , 2010 .

[26]  N. Cressie,et al.  Fixed rank kriging for very large spatial data sets , 2008 .

[27]  J. H. Wilkinson,et al.  Error analysis , 2003 .

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

[29]  A. Gelfand,et al.  Gaussian predictive process models for large spatial data sets , 2008, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[30]  H. Rue,et al.  An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .

[31]  Justus Notholt,et al.  The Total Carbon Column Observing Network , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[33]  Justus Notholt,et al.  Calibration of TCCON column-averaged CO2: the first aircraft campaign over European TCCON sites , 2011 .

[34]  B. Connor,et al.  Quantification of uncertainties in OCO-2 measurements of XCO 2 : simulations and linear error analysis , 2016 .

[35]  Colm Sweeney,et al.  AirCore: An Innovative Atmospheric Sampling System , 2010 .

[36]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part II: Global X CO 2 data characterization , 2012 .

[37]  C. Sweeney,et al.  Lower-tropospheric CO 2 from near-infrared ACOS-GOSAT observations , 2016 .