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Spatial statistical data fusion for remote sensing applications
Faculty of Informatics - Papers (Archive)
  • Hai Nguyen, Caltech
  • Noel A Cressie, University of Wollongong
  • Amy Braverman, California Institute of Technology
Publication Date
Publication Details
Nguyen, H., Cressie, N. A. & Braverman, A. (2012). Spatial statistical data fusion for remote sensing applications. Journal of the American Statistical Association, 107 (499), 1004-1018.
Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models, and it is crucial to accurately estimate aerosol concentrations from remote sensing instruments so as to minimize errors "downstream" in climate models. Currently, space-based observations of aerosols are available from two remote sensing instruments on board NASA's Terra spacecraft: the Mu Wangle Imaging SpectroRadiometer (MISR), and the MODerate-resolution Imaging Spectrometer (MODIS). These two instruments have complementary coverage, spatial support, and retrieval characteristics, making it advantageous to combine information from both sources to make optimal inferences about global aerosol distributions.
Citation Information
Hai Nguyen, Noel A Cressie and Amy Braverman. "Spatial statistical data fusion for remote sensing applications" (2012) p. 1004 - 1018
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