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
Source: Remote Sensing of Environment. Unidade: ESALQ
Subjects: ESPECTROSCOPIA INFRAVERMELHA, FOLHAS (PLANTAS), SENSORIAMENTO REMOTO, VEGETAÇÃO
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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: 10 nov. 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.2018.11.002NLM
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 nov. 10 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002Vancouver
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 nov. 10 ] Available from: https://doi.org/10.1016/j.rse.2018.11.002
