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Space-Time Data FusionSpace-time Data Fusion (STDF) is a methodology for combing heterogeneous remote sensing data to optimally estimate the true values of a geophysical field of interest, and obtain uncertainties for those estimates. The input data sets may have different observing characteristics including different footprints, spatial resolutions and fields of view, orbit cycles, biases, and noise characteristics. Despite these differences all observed data can be linked to the underlying field, and therefore the each other, by a statistical model. Differences in footprints and other geometric characteristics are accounted for by parameterizing pixel-level remote sensing observations as spatial integrals of true field values lying within pixel boundaries, plus measurement error. Both spatial and temporal correlations in the true field and in the observations are estimated and incorporated through the use of a space-time random effects (STRE) model. Once the models parameters are estimated, we use it to derive expressions for optimal (minimum mean squared error and unbiased) estimates of the true field at any arbitrary location of interest, computed from the observations. Standard errors of these estimates are also produced, allowing confidence intervals to be constructed. The procedure is carried out on a fine spatial grid to approximate a continuous field. We demonstrate STDF by applying it to the problem of estimating CO2 concentration in the lower-atmosphere using data from the Atmospheric Infrared Sounder (AIRS) and the Japanese Greenhouse Gasses Observing Satellite (GOSAT) over one year for the continental US.
Document ID
20150007205
Document Type
Conference Paper
External Source(s)
Authors
Braverman, Amy (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Nguyen, Hai (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Olsen, Edward (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Cressie, Noel (Ohio State Univ. Columbus, OH, United States)
Date Acquired
May 4, 2015
Publication Date
June 21, 2011
Subject Category
Geophysics
Statistics and Probability
Earth Resources and Remote Sensing
Meeting Information
2011 Earth Science Technology Forum (ESTF2011)(Pasadena, CA)
Distribution Limits
Public
Copyright
Other
Keywords
Greenhouse Gases Observing Satellite (GOSAT)
spatio-temproal statistics
Atmospheric Infrared Sounder (AIRS)
data fusion
carbon dioxide