Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605−720), the sum of reconstructed detail reflectance at fourth decomposition level over 605−720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350−2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = －0.719, p < 0.001). based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean r2val (determination coefficient of validation) value of 0.706 and an rpd (residual prediction deviation) value of 1.75 for cu estimation. the bootstrapping and anova test results showed that swt (605−720) significantly (p < 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. it was concluded here that the wavelet-based area parameter (i.e., swt (605−720)) has potential ability to indirectly estimate cu concentrations in carex leaves through the strong correlation between cu and chlorophyll. the method presented in this pilot study may be used to estimate the concentrations of other heavy metals. however, further research is needed to test its transferability and robustness for estimating cu concentrations on other plant species in different biological and environmental conditions.