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Activity Number: 22 - Statistical Methods for Heterogeneous and Massive Remote Sensing Data
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #323243
Title: Inference for Errors-In-Variables Models in the Presence of Spatial and Temporal Dependence with an Application to a Carbon Dioxide Remote Sensing Campaign
Author(s): Bohai Zhang* and Noel Cressie and Debra Wunch
Companies: University of Wollongong and University of Wollongong and University of Toronto
Keywords: Calibration ; estimating equations ; OCO-2 ; regression analysis ; spatial statistics ; TCCON

Satellite remote sensing measurements provide global coverage of Earth and its atmosphere within a matter of days, which helps scientists characterize and understand the spatio-temporal distribution of environmental processes. Because satellite data are obtained based on reflected energies from Earth's surface, the retrieved measurements usually have large variability that potentially contains biases. Calibration/validation is often achieved by modeling a linear relationship between the satellite data (Y) and other data sources (X). In this talk, we propose an errors-in-variables model for capturing this linear relationship, where errors are present in both X and Y. Consistent estimators of regression coefficients rely on unbiased estimating equations, which rely on statistical-dependence models from which the errors' variances can be calculated. We illustrate our calibration method through both simulated examples and an application to atmospheric column-averaged CO2 measurements (Y) from the Orbiting Carbon Observatory-2 (OCO-2). Here, the other data source (X) comes from ground-based measurements obtained from the Total Carbon Column Observing Network (TCCON).

Authors who are presenting talks have a * after their name.

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