Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/339685
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn231pt_BR
dc.contributor.authorunicampOliveira, Julianne de Castro-
dc.typeArtigopt_BR
dc.titleEstimating leaf mass per area and equivalent water thickness based on leaf optical properties : potential and limitations of physical modeling and machine learningpt_BR
dc.contributor.authorFeret, J-B.-
dc.contributor.authorle Maire, G.-
dc.contributor.authorJay, S.-
dc.contributor.authorBerveiller, D.-
dc.contributor.authorBendoula, R.-
dc.contributor.authorHmimina, G.-
dc.contributor.authorCheraiet, A.-
dc.contributor.authorOliveira, J. C.-
dc.contributor.authorPonzoni, F. J.-
dc.contributor.authorSolanki, T.-
dc.contributor.authorde Boissieu, F.-
dc.contributor.authorChave, J.-
dc.contributor.authorNouvellon, Y.-
dc.contributor.authorPorcar-Castell, A.-
dc.contributor.authorProisy, C.-
dc.contributor.authorSoudani, K.-
dc.contributor.authorGastellu-Etchegorry, J-P.-
dc.contributor.authorLefevre-Fonollosa, M-J.-
dc.subjectMáquina de vetores de suportept_BR
dc.subject.otherlanguageSupport vector machinespt_BR
dc.description.abstractLeaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT accurately and LMA poorly, while (2) statistically-based and machine learning (ML) methods provide accurate estimates of both LMA and EWT. Using six experimental datasets including broadleaf species samples of >150 species collected over tropical, temperate and boreal ecosystems, we compared the performances of a physically-based method (PROSPECT model inversion) and a ML algorithm (support vector machine regression, SVM) to infer EWT and LMA based on leaf reflectance and transmittance. We assessed several merit functions to invert PROSPECT based on iterative optimization and investigated the spectral domain to be used for optimal estimation of LMA and EWT. We also tested several strategies to select the training samples used by the SVM, in order to investigate the generalization ability of the derived regression models. We evidenced that using spectral information from 1700 to 2400 nm leads to strong improvement in the estimation of EWT and LMA when performing a PROSPECT inversion, decreasing the LMA and EWT estimation errors by 55% and 33%, respectively. The comparison of various sampling strategies for the training set used with SVM suggests that regression models show limited generalization ability, particularly when the regression model is applied on data fully independent from the training set. Finally, our results demonstrate that, when using an appropriate spectral domain, the PROSPECT inversion outperforms SVM trained with experimental data for the estimation of EWT and LMA. Thus we recommend that estimation of LMA and EWT based on leaf optical properties should be physically-based using inversion of reflectance and transmittance measurements on the 1700 to 2400 nm spectral rangept_BR
dc.relation.ispartofRemote sensing of environmentpt_BR
dc.relation.ispartofabbreviationRemote dens. environ.pt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2019-
dc.date.monthofcirculationSept.pt_BR
dc.language.isoengpt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0034-4257pt_BR
dc.identifier.eissn1879-0704pt_BR
dc.identifier.doi10.1016/j.rse.2018.11.002pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0034425718305030pt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsordocumentnumbernão tempt_BR
dc.date.available2020-04-27T16:51:08Z-
dc.date.accessioned2020-04-27T16:51:08Z-
dc.description.provenanceSubmitted by Mariana Aparecida Azevedo (mary1@unicamp.br) on 2020-04-27T16:51:08Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-07-30T19:31:48Z : No. of bitstreams: 1 000484643900065.pdf: 2536856 bytes, checksum: afc2878148f3baa3e2bdf6894f6ed056 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-04-27T16:51:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2018en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/339685-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.subject.keywordBiophysical propertiespt_BR
dc.subject.keywordLeaf spectroscopypt_BR
dc.subject.keywordEWTpt_BR
dc.subject.keywordLMApt_BR
dc.subject.keywordRadiative transfer modelpt_BR
dc.subject.keywordVegetationpt_BR
dc.identifier.source000484643900065pt_BR
dc.creator.orcid0000-0002-0852-1615pt_BR
dc.type.formArtigopt_BR
dc.identifier.articleid110959pt_BR
dc.description.sponsorNoteThe authors warmly thank Luc Bidel, Christophe François and Gabriel Pavan who collected the ANGERS dataset. This work was funded by the TOSCA program grant of the French Space Agency (CNES) (HyperTropik project); the International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT) (FLUO-SYNTHESIS project); the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES). ITATINGA genotype test is funded in part by the EUCFLUX project of Forestry Science and Research Institute (IPEF) and in part by SOERE F-ORE-T, which is supported annually by Ecofor, Allenvi, and the ANAEE-F. This study has benefitted from "Investissement d’Avenir" grants managed by Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01; ANAEE-France: ANR-11-INBS-0001). The authors also thank Valentine Alt, Samuel Counil, and Philippe Gaucher for tree climbing and data collection, as well as Anna Grandchamp for her help during data collection in Nouragues field stationpt_BR
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