Chlorophyll content in canopy plays an important role in reflecting the growing status of vegetation. To achieve high accuracy of chlorophyll content estimation based on hyperspectral data, the spectral reflectance and chlorophyll content in cotton canopy are measured from field observation. Original spectral data is transformed to calculate the hyperspectral parameters. The correlation between hyperspectral parameters and chlorophyll content is analyzed and a back propagation (BP) neural network model for estimating chlorophyll content in cotton canopy is established. Results show that after continuum-removal transformation, the correlation between canopy reflectance and chlorophyll content improves by 10.7% in the spectral bands of 560-740 nm, which is better than that of the original spectrum and the first-order differential spectrum. Vegetation indices, such as mSR, mND, NDI, and DD, which are established using the original spectrum and continuum-removal spectrum, show a high correlation with chlorophyll content under both spectral conditions with a correlation coefficient of approximately 0.8. In the BP neural network model, the model determination coefficient based on continuum spectral indices is 0.85, and the root-mean-square error and relative error are 1.37 and 1.97%, respectively. This result is better than that of the model based on red-edge parameters, original spectral vegetation indices, and first-order differential spectral indices. This study provides important theoretical basis and technical support for practical application of chlorophyll content estimation in crops.