LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiomet...

Full description

Saved in:
Bibliographic Details
Published in:ISPRS journal of photogrammetry and remote sensing Vol. 63; no. 4; pp. 409 - 426
Main Authors: Darvishzadeh, Roshanak, Skidmore, Andrew, Schlerf, Martin, Atzberger, Clement, Corsi, Fabio, Cho, Moses
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2008
Elsevier Science
Subjects:
Animal, plant and microbial ecology
Applied geophysics
band vegetation indexes
Biological and medical sciences
canopy reflectance
Chlorophyll
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Grassland
Hyperspectral remote sensing
Internal geophysics
Leaf area index
multiple linear-regression
nitrogen status
Partial least squares regression
radiative-transfer models
red edge position
reflectance data
remote-sensing data
resolution satellite data
Teledetection and vegetation maps
Narrows
New York
United States
Grassland
Chlorophyll
Partial least squares regression
Hyperspectral remote sensing
Leaf area index
wavelength
models
reflectance
Mediterranean vegetation
chlorophyll
accuracy
remote sensing
least-squares
Plant leaf
North America
in situ
grasslands
regression
calibration
errors
Canopy(vegetation)
ISSN:0924-2716, 1872-8235
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy ( R cv 2 = 0.74 , nRMSE cv = 0.35 ). All methods failed to estimate LCC ( R cv 2 ≤ 0.40 ), while LAI was estimated with intermediate accuracy ( R cv 2 values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics.
ISSN:0924-2716
1872-8235
DOI:10.1016/j.isprsjprs.2008.01.001