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http://hdl.handle.net/2014/41967
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| Title: | Space-time data fusion for remote sensing applications |
| Authors: | Braverman, Amy
Nguyen, H.
Cressie, N. |
| Keywords: | Orbiting Carbon Observatory
viewing geometry
data fusion
spatio-temporal statistics
uncertainty qualification
massive datasets
carbon dioxide |
| Issue Date: | 10-Apr-2011 |
| Publisher: | Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011 |
| Citation: | 34th International Symposium on Remote Sensing of Environment, Sydney, Australia, 10-15 April 2011 |
| Abstract: | NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets. |
| URI: | http://hdl.handle.net/2014/41967 |
| Appears in Collections: | JPL TRS 1992+
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