Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning-Reference-Cited by-同舟云学术

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

Published:2019-09 Issue: Volume:231 Page:110959
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Féret J.-B.^ORCID,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.,Lefèvre-Fonollosa M.-J.

Funder

CNES

International Network for Terrestrial Research and Monitoring in the Arctic

Coordination for the Improvement of Higher Education Personnel

Agence Nationale de la Recherche

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference93 articles.

1. Estimating leaf functional traits by inversion of PROSPECT: assessing leaf dry matter content and specific leaf area in mixed mountainous forest;Ali;Int. J. Appl. Earth Obs. Geoinf.,2016

2. Interaction of isotropic light with a compact plant leaf;Allen;J. Opt. Soc. Am.,1969