Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data

  • 1. State Key Laboratory for Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China

Received date: 2013-07-04

  Revised date: 2013-08-07

  Online published: 2013-09-29


Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.

Cite this article

LIU Xiang, LIU Rong-Gao, CHEN Jing-Meng, CHENG Xiao, ZHENG Guang . Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data[J]. Journal of Geo-information Science, 2013 , 15(5) : 734 -743 . DOI: 10.3724/SP.J.1047.2013.00734


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