@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. ",
}