Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

K Cawse-Nicholson; A M Raiho; D R Thompson; G C Hulley; C E Miller; K R Miner; B Poulter; D Schimel; F D Schneider; P A Townsend; S K Zareh

doi:10.1029/2022JG006876

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

J Geophys Res Biogeosci. 2022 Aug;127(8):e2022JG006876. doi: 10.1029/2022JG006876. Epub 2022 Aug 12.

Authors

K Cawse-Nicholson¹, A M Raiho^{2

3}, D R Thompson¹, G C Hulley¹, C E Miller¹, K R Miner¹, B Poulter², D Schimel¹, F D Schneider¹, P A Townsend^{1

4}, S K Zareh¹

Affiliations

¹ Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA.
² NASA Goddard Space Flight Center Biospheric Sciences Lab Greenbelt MD USA.
³ Earth System Science Interdisciplinary Center University of Maryland College Park, Greenbelt MD USA.
⁴ University of Wisconsin Madison WI USA.

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 ratio. 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 ID 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. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Keywords: hyperspectral; imaging spectroscopy; intrinsic dimensionality; mission design; surface biology and geology.

© 2022 Jet Propulsion Laboratory and The Authors, California Institute of Technology. Government sponsorship acknowledged. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.