Modeling uncertainties in estimation of canopy LAI from hyperspectral remote sensing data – A Bayesian approach

Petri Varvia*, Miina Rautiainen, Aku Seppänen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


Hyperspectral remote sensing data carry information on the leaf area index (LAI) of forests, and thus in principle, LAI can be estimated based on the data by inverting a forest reflectance model. However, LAI is usually not the only unknown in a reflectance model; especially, the leaf spectral albedo and understory reflectance are also not known. If the uncertainties of these parameters are not accounted for, the inversion of a forest reflectance model can lead to biased estimates for LAI. In this paper, we study the effects of reflectance model uncertainties on LAI estimates, and further, investigate whether the LAI estimates could recover from these uncertainties with the aid of Bayesian inference. In the proposed approach, the unknown leaf albedo and understory reflectance are estimated simultaneously with LAI from hyperspectral remote sensing data. The feasibility of the approach is tested with numerical simulation studies. The results show that in the presence of unknown parameters, the Bayesian LAI estimates which account for the model uncertainties outperform the conventional estimates that are based on biased model parameters. Moreover, the results demonstrate that the Bayesian inference can also provide feasible measures for the uncertainty of the estimated LAI.

Original languageEnglish
Pages (from-to)19-29
Number of pages11
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Publication statusPublished - 1 Apr 2017
MoE publication typeA1 Journal article-refereed


  • Leaf area index
  • Photon recollision probability
  • Reflectance model
  • Spectral invariants
  • Uncertainty quantification

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