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Activity Number: 666
Type: Invited
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #307122
Title: Multivariate Spatial Data Fusion for Global Remote Sensing Data Sets
Author(s): Hai Minh Nguyen*+ and Matthias Katzfuss and Noel Cressie and Amy Braverman
Companies: Jet Propulsion Laboratory and Universität Heidelberg and National Institute for Applied Statistics Research Australia and Jet Propulsion Laboratory
Keywords: remote sensing CO2 ; Fixed Rank Smoothing ; EM algorithm ; multivariate geostatistics
Abstract:

Developing global maps of carbon dioxide (CO2) concentration near the Earth's surface can help identify locations where major amounts of CO2 are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse effect of atmospheric CO2. Existing satellite remote sensing data do not provide measurements of the CO2 concentration near the surface. Japan's Greenhouse gases Observing SATellite (GOSAT) is sensitive to the mean CO2 concentration in the entire column, and NASA's Atmospheric InfraRed Sounder (AIRS) measures the CO2 in the middle troposphere.

One might expect that lower-atmospheric CO2 could be inferred by differencing GOSAT total column and AIRS mid-tropospheric data. We describe a spatio-temporal data-fusion (STDF) methodology based on reduced-dimensional Kalman smoothing. Our STDF is able to combine the complementary GOSAT and AIRS datasets to optimally estimate lower-atmospheric CO2 concentration over the whole globe. Further it is designed for massive remote sensing datasets and accounts for differences in instrument footprint, measurement-error characteristics, and data coverages.


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