dc.contributor.author | Annala, Leevi | |
dc.contributor.author | Honkavaara, Eija | |
dc.contributor.author | Tuominen, Sakari | |
dc.contributor.author | Pölönen, Ilkka | |
dc.date.accessioned | 2020-01-28T11:57:42Z | |
dc.date.available | 2020-01-28T11:57:42Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Annala, L., Honkavaara, E., Tuominen, S., & Pölönen, I. (2020). Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. <i>Remote Sensing</i>, <i>12</i>(2), Article 283. <a href="https://doi.org/10.3390/rs12020283" target="_blank">https://doi.org/10.3390/rs12020283</a> | |
dc.identifier.other | CONVID_34423785 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/67577 | |
dc.description.abstract | Miniaturized 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.mimetype | application/pdf | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | MDPI AG | |
dc.relation.ispartofseries | Remote Sensing | |
dc.rights | CC BY 4.0 | |
dc.subject.other | optical properties | |
dc.subject.other | convolutional neural network | |
dc.subject.other | deep learning | |
dc.subject.other | chlorophyll | |
dc.subject.other | stochastic modeling | |
dc.subject.other | physical parameter retrieval | |
dc.subject.other | forestry | |
dc.title | Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202001281831 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 2072-4292 | |
dc.relation.numberinseries | 2 | |
dc.relation.volume | 12 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2020 by the authors | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.grantnumber | 1711/31/2016 | |
dc.subject.yso | stokastiset prosessit | |
dc.subject.yso | optiset ominaisuudet | |
dc.subject.yso | klorofylli | |
dc.subject.yso | metsänhoito | |
dc.subject.yso | spektrikuvaus | |
dc.subject.yso | neuroverkot | |
dc.subject.yso | kaukokartoitus | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | metsänarviointi | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p11400 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p25870 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3007 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7534 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26364 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7292 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p2521 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p18894 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.3390/rs12020283 | |
dc.relation.funder | TEKES | en |
dc.relation.funder | TEKES | fi |
jyx.fundingprogram | Public research networked with companies, TEKES | en |
jyx.fundingprogram | Elinkeinoelämän kanssa verkottunut tutkimus, TEKES | fi |
jyx.fundinginformation | This research was funded by the Finnish Funding Agency for Innovation Tekes grant number 1711/31/2016. | |
dc.type.okm | A1 | |