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

J-B. Feret; G. le Maire; S. Jay; D. Berveiller; R. Bendoula; G. Hmimina; A. Cheraiet; J. C. Oliveira; F. J. Ponzoni; T. Solanki; F. de Boissieu; J. Chave; Y. Nouvellon; A. Porcar-Castell; C. Proisy; K. Soudani; J-P. Gastellu-Etchegorry; M-J. Lefevre-Fonollosa

doi:10.1016/j.rse.2018.11.002

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

J-B. Feret, G. le Maire, S. Jay, D. Berveiller, R. Bendoula, G. Hmimina, A. Cheraiet, J. C. Oliveira, F. J. Ponzoni, T. Solanki, F. de Boissieu, J. Chave, Y. Nouvellon, A. Porcar-Castell, C. Proisy, K. Soudani, J-P. Gastellu-Etchegorry, M-J. Lefevre-Fonollosa

Research output: Contribution to journal › Article › Scientific › peer-review

Original language	English
Article number	110959
Journal	Remote Sensing of Environment
Volume	231
Number of pages	14
ISSN	0034-4257
DOIs	https://doi.org/10.1016/j.rse.2018.11.002
Publication status	Published - 15 Sept 2019
MoE publication type	A1 Journal article-refereed

Fields of Science

Biophysical properties
Leaf spectroscopy
EWT
LMA
Radiative transfer model
Support vector machine
Vegetation
FUEL MOISTURE-CONTENT
DRY-MATTER CONTENT
RELATIVE GROWTH-RATE
PROSPECT MODEL
FUNCTIONAL TRAITS
HYPERSPECTRAL INDEXES
CONCEPTUAL-FRAMEWORK
SPECTRAL INDEXES
BROAD-LEAF
REFLECTANCE
1172 Environmental sciences
4112 Forestry

Access to Document

10.1016/j.rse.2018.11.002

Cite this

Feret, J.-B., le Maire, G., Jay, S., Berveiller, D., Bendoula, R., Hmimina, G., Cheraiet, A., Oliveira, J. C., Ponzoni, F. J., Solanki, T., de Boissieu, F., Chave, J., Nouvellon, Y., Porcar-Castell, A., Proisy, C., Soudani, K., Gastellu-Etchegorry, J.-P., & Lefevre-Fonollosa, M.-J. (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, Article 110959. https://doi.org/10.1016/j.rse.2018.11.002

@article{e7c8b566232d4bb6ae9d6c30e313a4fd,

title = "Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning",

keywords = "Biophysical properties, Leaf spectroscopy, EWT, LMA, Radiative transfer model, Support vector machine, Vegetation, FUEL MOISTURE-CONTENT, DRY-MATTER CONTENT, RELATIVE GROWTH-RATE, PROSPECT MODEL, FUNCTIONAL TRAITS, HYPERSPECTRAL INDEXES, CONCEPTUAL-FRAMEWORK, SPECTRAL INDEXES, BROAD-LEAF, REFLECTANCE, 1172 Environmental sciences, 4112 Forestry",

author = "J-B. Feret and {le Maire}, G. and S. Jay and D. Berveiller and R. Bendoula and G. Hmimina and A. Cheraiet and Oliveira, {J. C.} and Ponzoni, {F. J.} and T. Solanki and {de Boissieu}, F. and J. Chave and Y. Nouvellon and A. Porcar-Castell and C. Proisy and K. Soudani and J-P. Gastellu-Etchegorry and M-J. Lefevre-Fonollosa",

year = "2019",

month = sep,

day = "15",

doi = "10.1016/j.rse.2018.11.002",

language = "English",

volume = "231",

journal = "Remote Sensing of Environment",

issn = "0034-4257",

publisher = "Elsevier Inc. ",

}

Feret, J-B, le Maire, G, Jay, S, Berveiller, D, Bendoula, R, Hmimina, G, Cheraiet, A, Oliveira, JC, Ponzoni, FJ, Solanki, T, de Boissieu, F, Chave, J, Nouvellon, Y, Porcar-Castell, A, Proisy, C, Soudani, K, Gastellu-Etchegorry, J-P & Lefevre-Fonollosa, M-J 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, vol. 231, 110959. https://doi.org/10.1016/j.rse.2018.11.002

TY - JOUR

T1 - Estimating leaf mass per area and equivalent water thickness based on leaf optical properties

T2 - Potential and limitations of physical modeling and machine learning

AU - Feret, J-B.

AU - le Maire, G.

AU - Jay, S.

AU - Berveiller, D.

AU - Bendoula, R.

AU - Hmimina, G.

AU - Cheraiet, A.

AU - Oliveira, J. C.

AU - Ponzoni, F. J.

AU - Solanki, T.

AU - de Boissieu, F.

AU - Chave, J.

AU - Nouvellon, Y.

AU - Porcar-Castell, A.

AU - Proisy, C.

AU - Soudani, K.

AU - Gastellu-Etchegorry, J-P.

AU - Lefevre-Fonollosa, M-J.

PY - 2019/9/15

Y1 - 2019/9/15

KW - Biophysical properties

KW - Leaf spectroscopy

KW - EWT

KW - LMA

KW - Radiative transfer model

KW - Support vector machine

KW - Vegetation

KW - FUEL MOISTURE-CONTENT

KW - DRY-MATTER CONTENT

KW - RELATIVE GROWTH-RATE

KW - PROSPECT MODEL

KW - FUNCTIONAL TRAITS

KW - HYPERSPECTRAL INDEXES

KW - CONCEPTUAL-FRAMEWORK

KW - SPECTRAL INDEXES

KW - BROAD-LEAF

KW - REFLECTANCE

KW - 1172 Environmental sciences

KW - 4112 Forestry

U2 - 10.1016/j.rse.2018.11.002

DO - 10.1016/j.rse.2018.11.002

M3 - Article

SN - 0034-4257

VL - 231

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

M1 - 110959

ER -