Spatio-Temporal Data Fusion for Massive Multivariate Remote Sensing Data

• Activity Number: 24
• Type: Topic Contributed
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Hai Nguyen* and Matthias Katzfuss and Noel Cressie and Amy Braverman
• Companies: Jet Propulsion Laboratory and Texas A&M University and University of Wollongong and Jet Propulsion Laboratory
• Keywords: massive dataset ; remote sensing ; kalman smoother ; spatial random effects

Abstract

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. However, 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 are available, and in principle inferences can be made by considering a weighted difference between the total-column and mid-tropospheric CO2.

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.