TY  - JOUR
AU  - Zhang, J.H.
AU  - Du, Y.Z.
AU  - Liu, X.F.
AU  - He, Z.M.
AU  - Yang, Limin
PY  - 2012//
TI  - Progress in leaf area index retrieval based on hyperspectral remote sensing and retrieval models
JO  - Spectroscopy and Spectral Analysis
SP  - 3319
EP  - 3323
VL  - 32
IS  - 12
KW  - airborne
KW  - area
KW  - basic
KW  - biophysical
KW  - biophysical parameter
KW  - canopy
KW  - carbon
KW  - China
KW  - component
KW  - crop yield
KW  - data
KW  - development
KW  - ecological
KW  - ecosystem
KW  - estimation
KW  - flux
KW  - ground-based measurement
KW  - hyperspectral remote sensing
KW  - journal articles
KW  - LAI
KW  - land surface
KW  - landscape
KW  - leaf area index
KW  - mapping
KW  - measurement
KW  - methodology
KW  - model
KW  - modeling
KW  - processes
KW  - remote sensing
KW  - research
KW  - research program
KW  - retrieval model
KW  - sensor
KW  - simulation
KW  - spatially explicit
KW  - structure
KW  - technique
KW  - water
KW  - yield
N2  - The leaf area index (LAI) is a very important parameter affecting land-atmosphere exchanges in land-surface processes; LAI is one of the basic feature parameters of canopy structure, and one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. To detect LAI accurately and quickly is one of tasks in the ecological and agricultural crop yield estimation study, etc. Emerging hyperspectral remote sensing sensor and techniques can complement existing ground-based measurement of LAI. Spatially explicit measurements of LAI extracted from hyperspectral remotely sensed data are component necessary for simulation of ecological variables and processes. This paper firstly summarized LAI retrieval method based on different level hyperspectral remote sensing platform (i. e., airborne, satellite-borne and ground-based); and secondly different kinds of retrieval model were summed up both at home and abroad in recent years by using hyperspectral remote sensing data; and finally the direction of future development of LAI remote sensing inversion was analyzed.
SN  - http://www.gpxygpfx.com/en/gkll.asp
UR  - http://www.gpxygpfx.com/en/gkll.asp
N1  - exported from refbase (http://eros.usgs.gov/refbase/show.php?record=25697), last updated on Thu, 18 Apr 2013 08:59:43 -0500
ID  - Zhang_etal2012
ER  -