loading page

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters
  • +8
  • Kerry Cawse-Nicholson,
  • Ann Raiho,
  • David Ray Thompson,
  • Glynn Hulley,
  • Charles E. Miller,
  • Kimberley Miner,
  • Benjamin Poulter,
  • David Schimel,
  • Fabian Schneider,
  • Philip A Townsend,
  • Shannon-Kian Zareh
Kerry Cawse-Nicholson
Jet Propulsion Laboratory, California Institute of Technology

Corresponding Author:[email protected]

Author Profile
Ann Raiho
NASA Goddard Space Flight Center
Author Profile
David Ray Thompson
Jet Propulsion Laboratory, California Institute of Technology
Author Profile
Glynn Hulley
Jet Propulsion Laboratory, California Institute of Technology
Author Profile
Charles E. Miller
Jet Propulsion Laboratory
Author Profile
Kimberley Miner
Jet Propulsion Laboratory
Author Profile
Benjamin Poulter
NASA
Author Profile
David Schimel
Jet Propulsion Laboratory
Author Profile
Fabian Schneider
California Institue of Technology
Author Profile
Philip A Townsend
University of Wisconsin-Madison
Author Profile
Shannon-Kian Zareh
Jet Propulsion Laboratory, California Institute of Technology
Author Profile

Abstract

High-resolution space-based spectral imaging of the Earth’s surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise. The different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA’s Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the intrinsic dimensionality decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. Intrinsic dimensionality is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.