Uncertainty Quantification for Remote Sensing Data

• Activity Number: 280 - Climate Statistics: Studies on the Physics and Impacts of Climate Change Using Data Science
• Type: Topic Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Amy Braverman* and Jonathan Hobbs
• Companies: Jet Propulsion Laboratory and Jet Propulsion Laboratory
• Keywords: remote sensing ; uncertainty quantification ; big data ; Monte Carlo simulation ; Gaussian mixtures

Abstract

Satellite remote sensing instruments do not observe geophysical quantities of interest directly. They observe radiances in bands of the electromagnetic spectrum that are sensitive to those quantities. Geophysical data sets produced by NASA and other space agencies are the results of complex algorithms that infer geophysical state from observed radiances using ``retrieval" algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach and provide some real-life examples from JPL missions.