Spatio-Temporal Data Fusion for Remote-Sensing Applications

• Activity Number: 519
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Hai Nguyen*+ and Matthias Katzfuss and Noel Cressie and Amy Braverman
• Companies: California Institute of Technology and Universität Heidelberg and The Ohio State University and California Institute of Technology
• Keywords: change-of-support ; EM Algorithm ; Fixed rank kriging ; Kalman smoother ; remote sensing

Abstract

Developing global maps of carbon dioxide (CO2) concentration near the surface can help identify locations where major amounts of CO2 are entering and exiting the atmosphere. No single instrument currently provides this information, but inferences can be made by considering a weighted difference between total column CO2 concentration observed by the Greenhouse gases Observing Satellite (GOSAT) and mid-tropospheric CO2 concentration observed by the Atmospheric InfraRed Sounder (AIRS) on the Aqua satellite.

In the past, attempts to combine satellite information have been hindered by the instruments' different spatial supports and by the typically massive size of the remote sensing datasets. We describe a spatio-temporal data-fusion methodology, based on the Kalman filter and smoother, that can combine complementary datasets from multiple sources and properly account for spatial and temporal dependencies in order to produce more complete and accurate inferences. The resulting optimal predictors have computational complexity that is linear with respect to the number of observations at each time point.