Amy Braverman: Uncertainty Quantification for Remote Sensing Data

Amy Braverman (Jet Propulsion Laboratory, California Institute of Technology)

The ability of spaceborne remote sensing data to address important Earth and climate science problems rests crucially on how well the underlying geophysical quantities can be inferred from these observations. Remote sensing instruments measure parts of the electromagnetic spectrum and use computational algorithms to infer the unobserved true physical states. However, the accompanying uncertainties, if they are provided at all, are usually incomplete. There are many reasons why including but not limited to unknown physics, computational artifacts and compromises, unknown uncertainties in the inputs, and more.

In this talk I will discuss two approaches to uncertainty quantification for remote sensing data. The first is a practical methodology currently being implemented for NASA's Orbiting Carbon Observatory 2 and 3 missions. The method combines Monte Carlo simulation experiments with statistical modeling to approximate conditional distributions of unknown true states given point estimates produced by imperfect operational algorithms. Alternatively, the second approach explicitly leverages and accounts for spatial correlation in the underlying geophysical processes. This approach is more computationally demanding, but offers certain advantages that will be important for upcoming missions like Surface Biology and Geology (SBG) which will yield huge data volumes. I will review our approach and progress in spatial uncertainty quantification, and demonstrate with data from an SBG precursor mission called EMIT, currently on the International Space Station.