Recent studies have demonstrated that the decomposition of hyperspectral data using wavelet analysis is able to generate wavelet coefficients that can be used for estimating leaf chlorophyll (chl) concentrations. However, there is considerable scope for refining such techniques and this study addresses this issue by identifying the optimal spectral domain for use in constructing predictive models. Leaf reflectance spectra were simulated with the PROSPECT model (a model of leaf optical properties spectra) using randomly selected values for the input parameters. From reflectance and first derivative spectra different spectral wavelength domains were extracted, ranging from 400–450 to 400–2500 nm, using increments of 50 nm for the upper wavelength limit. Using the data for each wavelength domain, continuous wavelet decomposition was applied using 53 different wavelets, in turn. The resulting wavelet coefficients, from scales 1 to 128, were used as independent factors to construct predictive models for leaf chl concentration. Wavelet coefficients (at a specific scale generated by a given wavelet) in the chl absorption region remain constant when using spectral wavelength domains of 400–900 nm and broader, but narrower domains cause variability in the coefficients. Lower scale wavelet coefficients (scales 1–32) contain little information on chl concentration and their predictive performance does not vary with the spectral wavelength domain used. The higher scale wavelet coefficients (scales 64 and 128) can capture information on chl concentration, and predictive capability increases rapidly when the spectral wavelength domains vary from 400–700 to 400–900 nm but it can decrease or fluctuate for broader domains. In terms of accuracy and computational efficiency, models derived from the spectral wavelength domain 400–900 nm which use wavelet coefficients from scale 64 are optimal and a range of wavelet functions are suitable for performing the decomposition. The importance of optimizing the spectral wavelength domain highlighted by these findings has broader significance for the use of wavelet decomposition of hyperspectral data in quantifying other vegetation biochemicals and in other remote sensing applications.