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dc.contributor.authorVarvia P
dc.contributor.authorRautiainen M
dc.contributor.authorSeppänen A
dc.date.accessioned2017-07-21T10:34:22Z
dc.date.available2017-07-21T10:34:22Z
dc.date.issued2017
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/3698
dc.description.abstractHyperspectral 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.en
dc.language.isoENen
dc.publisherElsevier BVen
dc.relation.ispartofseriesJOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFERen
dc.relation.urihttps://doi.org/10.1016/j.jqsrt.2017.01.029en
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectLeaf area indexen
dc.subjectSpectral invariantsen
dc.subjectPhoton recollision probabilityen
dc.subjectReflectance modelen
dc.subjectUncertainty quantificationen
dc.titleModeling uncertainties in estimation of canopy LAI from hyperspectral remote sensing data - A Bayesian approachen
dc.description.versionfinal draften
dc.contributor.departmentDepartment of Applied Physics, activitiesen
uef.solecris.id46601790en
dc.type.publicationinfo:eu-repo/semantics/articleen
dc.rights.accessrights© Elsevier B.Ven
dc.relation.doi10.1016/j.jqsrt.2017.01.029en
dc.description.reviewstatuspeerRevieweden
dc.format.pagerange19-29en
dc.relation.issn0022-4073en
dc.relation.volume191en
dc.rights.accesslevelopenAccessen
dc.type.okmA1en
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


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