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dc.contributor.authorAnnala, Leevi
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorTuominen, Sakari
dc.contributor.authorPölönen, Ilkka
dc.date.accessioned2020-01-28T11:57:42Z
dc.date.available2020-01-28T11:57:42Z
dc.date.issued2020
dc.identifier.citationAnnala, Leevi; Honkavaara, Eija; Tuominen, Sakari; Pölönen, Ilkka (2020). Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. Remote Sensing, 12 (2), 283. DOI: 10.3390/rs12020283
dc.identifier.otherCONVID_34423785
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67577
dc.description.abstractMiniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARIOSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARIOSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.publisherMDPI AG
dc.relation.ispartofseriesRemote Sensing
dc.rightsCC BY 4.0
dc.subject.otheroptical properties
dc.subject.otherconvolutional neural network
dc.subject.otherdeep learning
dc.subject.otherchlorophyll
dc.subject.otherstochastic modeling
dc.subject.otherphysical parameter retrieval
dc.subject.otherforestry
dc.titleChlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202001281831
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn2072-4292
dc.relation.numberinseries2
dc.relation.volume12
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 by the authors
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber1711/31/2016
dc.subject.ysostokastiset prosessit
dc.subject.ysooptiset ominaisuudet
dc.subject.ysoklorofylli
dc.subject.ysometsänhoito
dc.subject.ysospektrikuvaus
dc.subject.ysoneuroverkot
dc.subject.ysokaukokartoitus
dc.subject.ysokoneoppiminen
dc.subject.ysometsänarviointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p11400
jyx.subject.urihttp://www.yso.fi/onto/yso/p25870
jyx.subject.urihttp://www.yso.fi/onto/yso/p3007
jyx.subject.urihttp://www.yso.fi/onto/yso/p7534
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p18894
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/rs12020283
dc.relation.funderTEKESfi
dc.relation.funderTEKESen
jyx.fundingprogramElinkeinoelämän kanssa verkottunut tutkimus, TEKESfi
jyx.fundingprogramPublic research networked with companies, TEKESen
jyx.fundinginformationThis research was funded by the Finnish Funding Agency for Innovation Tekes grant number 1711/31/2016.


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