Spectral invariants in ultra-high spatial resolution hyperspectral images

Olli Ihalainen (Corresponding Author), Matti Mõttus

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
5 Downloads (Pure)


Current operational optical satellite-based Earth observation methods targeting at vegetation are optimised for the high- and medium-resolution satellites with pixels sizes starting at ten meters. This resolution is coarser than the typical size of a vegetation structural element, such as a tree crown, and much coarser that of scattering elements, such as leaves. For this reason, vegetation can be treated as a continuous medium and the variation in the local illumination conditions on individual leaves or tree crowns can be ignored. This does not hold anymore for very and ultra-high resolution imagery, obtained from new satellite systems or unmanned aerial vehicles, where individual tree crowns or even leaves can be discerned. We tested the applicability of the spectral invariant theory to this type of imagery for characterising the local illumination conditions on plant leaves using Monte Carlo ray tracing simulations. The simulations corroborated the direct link between the spectral invariant parameter and the sunlit fraction of visible leaves, earlier alleged for hyperspectral remote sensing data based on mathematical considerations. The approach allowed us to separate the direct beam and multiple scattering irradiance components and provided intuitive interpretations of the recollision probability and canopy scattering coefficient computed for each image pixel.
Original languageEnglish
Article number108265
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Publication statusPublished - Sept 2022
MoE publication typeA1 Journal article-refereed


  • High resolution imaging spectroscopy
  • Hyper spectral remote sensing
  • Monte Carlo ray tracing
  • p-Theory
  • Shadow fraction
  • Spectral invariants
  • Sunlit fraction


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