Chlorophyll is essential to plant photosynthesis, and chlorophyll content is an important indicator of a plant’s growth status. During the past few decades, various types of spectral indices have been used to estimate chlorophyll content. Here we used a continuous wavelet transform (CWT) to estimate the chlorophyll content of maize leaves in different layers from visible to near-infrared (400 to 1000 nm) spectra. The dataset comprised 186 spectra from three leaf layers of plants under different nitrogen treatments. To identify the most sensitive wavelet features, wavelet power scalograms were generated by the CWT, then linear regression models were established between the wavelet power coefficients and chlorophyll content. Two individual wavelet features in the red-edge region were chosen for estimating the chlorophyll content of middle and lower layer, and all their determination coefficients (R 2 ) were better than the spectral indices. For the whole dataset, the most sensitive wavelet feature (724 nm, scale 4) was located near the red edge position, with better correlation (R 2 =90.50% ) than the best spectral index (R 2 =81.85% ). All the predicted models showed good consistency between the calibration and validation datasets, indicating that the chlorophyll content of different maize leaf layers can be accurately estimated by use of a CWT.