Kernel Flow Emulation for NASA's Surface Biology and Geology Mission

• Activity Number: 91 - Spatial Statistics and UQ: Foundations for Innovation in Environmental Science
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Amy Braverman* and Jouni Susiluoto and Houman Owhadi
• Companies: Jet Propulsion Laboratory, California Institute of Technology and Jet Propulsion Laboratory and California Institute of Technology
• Keywords: Emulator; kernel flows; spatial warping; remote sensing; Earth science; Surface Biology and Geology

Abstract

NASA's new SBG mission will launch in late 2026 and carry a hyperspectral imager to observe Earth's surface at high resolution (~30 meter) in the visible and thermal regions of the electromagnetic spectrum. Daily data volume is expected to be 2.5 to 5 petabytes. The mission's science objectives include understanding active surface changes, snow and ice accumulation, hazard risks, changing land use, plant physiology, and terrestrial and aquatic ecosystems. To meet these objectives, geophysical properties of Earth's surface must be inferred from observed spectra. Spectra are related to surface states via physical forward models embedded within inference algorithms. These forward models are computationally demanding, and will require emulation in order to keep up with data flow. In this talk we introduce a forward model emulator for SBG using a new method for fitting covariance parameters of Gaussian Processes, called Kernel Flows (KF; Owhadi and Yoo, 2019). KF uses a cross-validation approach and spatial warping, so it does not require stationarity or isotropy. Its innovation is in the computational algorithm, and its application in the remote sensing context.