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An overview of global leaf area index (LAI): Methods, products, validation, and applications

Authors :
Hongliang Fang
Gabriela Schaepman-Strub
Frédéric Baret
S. Plummer
Chinese Academy of Sciences (CAS)
Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH)
Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
European Space Agency (ESA)
Department of Evolutionary Biology and Environmental Studies
University of Zurich
Fang, Hongliang
Source :
Reviews of Geophysics, Reviews of Geophysics, American Geophysical Union, 2019, 737 (3), pp.739-799. ⟨10.1029/2018RG000608⟩, www.agu.org/journals/rg/
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation (EO) has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progress made in temporal compositing and prospects for future LAI estimation are discussed. Subsequently, the overview presents various LAI product validation schemes, uncertainties in global moderate resolution LAI products and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for under‐represented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, LiDAR technology, and unmanned aerial vehicles (UAV) be pursued to broaden the production and application of LAI.

Details

Language :
English
ISSN :
87551209
Database :
OpenAIRE
Journal :
Reviews of Geophysics, Reviews of Geophysics, American Geophysical Union, 2019, 737 (3), pp.739-799. ⟨10.1029/2018RG000608⟩, www.agu.org/journals/rg/
Accession number :
edsair.doi.dedup.....0a414c9aa9b1a3ece100dee90f4effcc
Full Text :
https://doi.org/10.1029/2018RG000608⟩