Remote sensing technology is an effective method of monitoring chlorophyll content, an important parameter for vegetation health. The chlorophyll information based on spectral information needs to consider the vertical characteristics of plants. Hyperspectral features [spectral reflectance (SR), spectral indices, and wavelet coefficients (WC)] were first selected to construct the cost function in the PROSPECT model-optimized inversion to improve the accuracy and efficiency of leaf chlorophyll content (LCC) inversion. Second, the sensitivity of LCC to leaf SR, vegetation index (VI), and WC were analyzed. Finally, LCC was inverted by the PROSPECT model using the iterative inversion algorithm, and the chlorophyll content of the vertical profile in maize was monitored. The following results were obtained. (1) According to the extended Fourier amplitude sensitivity test (EFAST) used to construct the cost-function inversion of LCC, the normalized difference vegetation index canste (NDVIcanste) and WC based on EFAST method which was used to construct the cost-function inversion of LCC, yielded higher accuracy than other spectral features. These two methods, combined with sensitivity analysis can provide accurate inversion results by weakening the influence of other parameters on spectral changes and eliminating the interference information between bands. (2) The cost function based on NDVIcanste in the LCC optimization exhibited overestimation, whereas WC can solve the problem efficiently. The continuous wavelet transform can extract weak information among the spectra, whereas the other two methods of SR and VI cannot easily obtain this information. (3) The vertical distribution of LCC in different maize varieties and treatments is in accordance with the parabola law, but the chlorophyll content of the different leaf positions on the vertical profile is somewhat different. The results reflect the vertical distribution of chlorophyll content through the radiation transfer of leaves, thus providing a theoretical basis for their further combination.
