Artigo de periodico

Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning (2019)

• Authors:
  • Feret, J-B
  • Maire, G. le
  • Jay, S
  • Berveiller, D
  • Bendoula, R
  • Hmimina, G
  • Cheraiet, A
  • Oliveira, J. C
  • Ponzoni, F. J
  • Solanki, T
  • Boissieu, F. de
  • Chave, J
  • Nouvellon, Yann Pierre
  • Porcar-Castell, A
  • Proisy, C
  • Soudani, K
  • Gastellu-Etchegorry, J-P
  • Lefevre-Fonollosa, M-J
• Autor USP: NOUVELLON, YANN PIERRE - ESALQ
• Unidade: ESALQ
• DOI: 10.1016/j.rse.2018.11.002
• Subjects: ESPECTROSCOPIA INFRAVERMELHA; FOLHAS (PLANTAS); SENSORIAMENTO REMOTO; VEGETAÇÃO
• Agências de fomento:
  • Luc Bidel, Christophe François and Gabriel Pavan
  • TOSCA program grant of the French Space Agency (CNES)
  • International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
  • ITATINGA genotype test is funded in part by the EUCFLUX
  • Agence Nationale de la Recherche
  • Nouragues Field Station
• Language: Inglês
• Imprenta:
  • Publisher place: Amsterdam
  • Date published: 2019
• Source:
  • Título do periódico: Remote Sensing of Environment
  • ISSN: 0034-4257
  • Volume/Número/Paginação/Ano: v. 231, p. 1-14, 2019

Informações sobre o DOI: 10.1016/j.rse.2018.11.002 (Fonte: oaDOI API)
• Este periódico é de assinatura
• Este artigo é de acesso aberto
• URL de acesso aberto
• Cor do Acesso Aberto: hybrid
• Licença: publisher-specific-oa

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• ABNT

  FERET, J-B et al. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, v. 231, p. 1-14, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.rse.2018.11.002. Acesso em: 19 abr. 2024.

• APA

  Feret, J. -B., Maire, G. le, Jay, S., Berveiller, D., Bendoula, R., Hmimina, G., et al. (2019). Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231, 1-14. doi:10.1016/j.rse.2018.11.002

• NLM

  Feret J-B, Maire G le, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, Boissieu F de, Chave J, Nouvellon YP, Porcar-Castell A, Proisy C, Soudani K, Gastellu-Etchegorry J-P, Lefevre-Fonollosa M-J. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning [Internet]. Remote Sensing of Environment. 2019 ; 231 1-14.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002

• Vancouver

  Feret J-B, Maire G le, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, Boissieu F de, Chave J, Nouvellon YP, Porcar-Castell A, Proisy C, Soudani K, Gastellu-Etchegorry J-P, Lefevre-Fonollosa M-J. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning [Internet]. Remote Sensing of Environment. 2019 ; 231 1-14.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002

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