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The information of vertical distribution of chlorophyll content in different leaf positions of crops was obtained scientifically and efficiently to facilitate monitoring of crop growth conditions and field management. Based on the hyperspectral reflectance and chlorophyll content of different leaf positions of winter wheat obtained during the heading period, the correlation analysis of raw spectra, first-order differential spectra, second-order differential spectra, vegetation indices, continuous wavelet coefficients and chlorophyll content were performed to screen the spectral feature parameters with strong correlation. Then partial least squares regression, support vector machine, random forest and back propagation neural network algorithms were employed to construct chlorophyll content estimation models for the upper 1, upper 2, upper 3 and upper 4 leaves of winter wheat, and the best models for chlorophyll content estimation at different leaf positions were screened based on the accuracy assessment results. The results showed that the chlorophyll content estimation models constructed using wavelet coefficients combined with partial least squares were the most accurate for the upper 1, upper 2 and upper 3 leaves, with modeling and validation R2 of 0.82 and 0.75, 0.80 and 0.77, 0.71 and 0.62, respectively; the chlorophyll content estimation models constructed using vegetation indices combined with support vector machine were the best for the upper 4 leaves, with modeling and validation R2 of 0.74 and 0.79, respectively. The research result could provide theoretical and technical support for accurate monitoring of the vertical variation characteristics of crop nutrient content based on remote sensing technology.