Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

Petri Varvia, Miina Rautiainen, Aku Seppänen

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)
60 Downloads (Pure)


In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.
Original languageEnglish
Pages (from-to)19-28
Number of pages10
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Leaf area index
  • Spectral invariants
  • Reflectance model
  • Uncertainty quantification
  • Seasonal dynamics


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