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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3TUJD25
Repositóriosid.inpe.br/mtc-m21c/2019/09.03.13.53   (acesso restrito)
Última Atualização2019:09.03.13.53.24 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/09.03.13.53.24
Última Atualização dos Metadados2020:01.06.11.42.19 (UTC) administrator
DOI10.1016/j.rse.2018.11.002
ISSN0034-4257
Chave de CitaçãoFéretMJBBHCOPSBCNPPSGL:2019:PoLiPh
TítuloEstimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning
Ano2019
MêsSept.
Data de Acesso12 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2393 KiB
2. Contextualização
Autor 1 Féret, J. B.
 2 Maire, G. le
 3 Jay, S.
 4 Berveiller, D.
 5 Bendoula, R.
 6 Hmimina, G.
 7 Cheraiet, A.
 8 Oliveira, J. C.
 9 Ponzoni, Flávio Jorge
10 Solanki, T.
11 Boissieu, F. de
12 Chave, J.
13 Nouvellon, Y.
14 Porcar-Castell, A.
15 Proisy, C.
16 Soudani, K.
17 Gastellu-Etchegorry, J. P.
18 Lefévre-Fonollosa, M. J.
Identificador de Curriculo 1
 2
 3
 4
 5
 6
 7
 8
 9 8JMKD3MGP5W/3C9JH4G
Grupo 1
 2
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 8
 9 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação 1 Université Montpellier
 2 CIRAD, UMR ECO&SOL
 3 Aix Marseille Univ
 4 University of Paris-Sud
 5 Université Montpellier
 6 University of Paris-Sud
 7 University of Paris-Sud
 8 Universidade Estadual de Campinas (UNICAMP0
 9 Instituto Nacional de Pesquisas Espaciais (INPE)
10 University of Helsinki
11 Université Montpellier
12 Université Paul Sabatier
13 CIRAD, UMR ECO&SOL
14 University of Helsinki
15 Univ. Montpellier
16 University of Paris-Sud
17 Centre d'Etudes Spatiales de la Biosphère
18 CNES
Endereço de e-Mail do Autor 1 jean-baptiste.feret@teledetection.fr
 2
 3
 4
 5
 6
 7
 8
 9 flavio.ponzoni@inpe.br
RevistaRemote Sensing of Environment
Volume231
Páginase110959
Nota SecundáriaA1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE
Histórico (UTC)2019-09-03 13:53:24 :: simone -> administrator ::
2019-09-03 13:53:24 :: administrator -> simone :: 2019
2019-09-03 13:56:31 :: simone -> administrator :: 2019
2020-01-06 11:42:19 :: administrator -> simone :: 2019
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveBiophysical properties
Leaf spectroscopy
EWT
LMA
Radiative transfer model
Support vector machine
Vegetation
ResumoLeaf 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 range.
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoferet_estimating.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher allowfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
Lista de Itens Citandosid.inpe.br/mtc-m21/2012/07.13.14.46.17 5
sid.inpe.br/bibdigital/2013/09.13.21.11 1
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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