Spectral dimensionality of imaging spectroscopy data over diverse landscapes and spatial resolutions

Jie Dai, Nicholas R. Vaughn, Megan Seeley, Joseph Heckler, David R. Thompson, Gregory P. Asner

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Spectral dimensionality reflects the intrinsic information content of imaging spectroscopy data. Prior work investigating spectral dimensionality has mostly either focused within a limited study area or at a single spatial resolution. Despite multiple spectrometer satellite missions in the development, the inherent information content of imaging spectroscopy images is still largely unknown. Leveraging data collected by the Global Airborne Observatory (GAO), we investigated the spectral dimensionality of common landscape types (i.e., urban, suburban, rural, agriculture, forest, shrub/grass, and wetland) across several spatial resolutions (i.e., 1 to 5, 10, 20, and 30 m). The results corresponded well to the size of the objects in the target landscape. At native spatial resolution (1 to 5 m), anthropogenic images dominated by fine-scale spectrally diverse objects usually had higher dimensionalities than natural images. But as resolution coarsened, the spectral dimensionality of images containing natural landscapes outranked anthropogenic ones, except for large-scale agriculture. Besides, there was a surprising difference in the magnitude of dimensionality decreasing trend between anthropogenic and natural scenes. Urban, suburban and rural cover types experienced dramatic drops at coarser resolutions, whereas vegetated landscapes preserved most of their dimensionality at 30-m spatial sampling. In addition to revealing the typical spectral dimensionalities of common landscapes, we provided insights into how much information might be achievable from spectroscopic analyses. Results of this study guide future imaging spectroscopy data collection and scientific applications.

Original languageEnglish (US)
Article number044518
JournalJournal of Applied Remote Sensing
Issue number4
StatePublished - Oct 1 2022


  • ground sampling distance
  • hyperspectral
  • imaging spectroscopy
  • spatial resolution
  • spectral dimensionality

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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