The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
A: rbiol.3 
B: derivative 
D: reflectance 
E: bior2.6 
F: derivative 
0 0.5 1 1.5 2 
Actual Protein (g/m 2 ) 
Figure 3. Actual versus predicted chemicals based on the 
optimum wavelet and non-wavelet-based models. All wavelet- 
based models are derived from continuous decompositions of 
reflectance spectra. For water, using A: the rbiorl.3 wavelet, 
coefficients from scale 1 and B: derivative spectra; for 
lignin/cellulose, using C: coifl, scale 2 and D: reflectance 
spectra; and for protein, using E: bior2.6, scale 4 and F: 
derivative spectra. In all cases eight predictors were permitted. 
4. CONCLUSIONS 
The results demonstrated that wavelet decomposition 
techniques can generate accurate predictions of protein, 
lignin/cellulose and water content, despite wide variations in of 
all of the biochemical and biophysical factors that influence leaf 
reflectance. Wavelet analysis outperformed predictive models 
based on untransformed spectra and enabled the greatest 
improvements in performance for protein followed by 
lignin/cellulose then water content. Hence, the study highlights 
the capabilities of wavelet decomposition for extracting 
information concerning leaf components that have narrow, 
weak absorption features, which are otherwise difficult to 
characterise in untransformed reflectance spectra. 
REFERENCES 
Blackburn, G.A. and Ferwerda, J.G. (2008) Retrieval of 
chlorophyll concentration from leaf reflectance spectra using 
wavelet analysis. Remote Sensing of Environment, 112, 1614— 
1632.