TY - JOUR

T1 - Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis.

AU - Blackburn, George Alan

AU - Ferwerda, Jelle Garke

PY - 2008/4/15

Y1 - 2008/4/15

N2 - The dynamics of foliar chlorophyll concentrations have considerable significance for plant–environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.

AB - The dynamics of foliar chlorophyll concentrations have considerable significance for plant–environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.

KW - Chlorophyll

KW - Leaf

KW - Reflectance

KW - Hyperspectral

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=40649110037&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2007.08.005

DO - 10.1016/j.rse.2007.08.005

M3 - Journal article

VL - 112

SP - 1614

EP - 1632

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

IS - 4

ER -