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Space-Time Data fusion for Remote Sensing ApplicationsNASA 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.
Document ID
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Braverman, Amy (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Nguyen, H. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Cressie, N. (Ohio State Univ. Columbus, OH, United States)
Date Acquired
August 25, 2013
Publication Date
April 10, 2011
Subject Category
Statistics and Probability
Meeting Information
34th International Symposium on Remote Sensing of Environment(Sydney)
Distribution Limits
carbon dioxide
uncertainty qualification
massive datasets
viewing geometry
Orbiting Carbon Observatory
data fusion
spatio-temporal statistics