The chlorophyll concentrations in different growth stages of winter wheat based on the effect of nitrogen fertilizer are studied, and the applicability of XGBoost algorithm in estimating the chlorophyll concentration of winter wheat is discussed. A hyperspectral estimation model for chlorophyll concentration in winter wheat is constructed using this algorithm which is compared with partial least squares and artificial neural network algorithms. The results show that: (1)The chlorophyll concentration of winter wheat increases gradually with the increase of nitrogen fertilizer. (2)The estimation model based on the first-order differential spectrum data set has the best performance. The XGBoost algorithm is found to work best by comparing R² and RPD of the modeling and verification data sets.(3)Through the band importance analysis, it is found that the 8 important bands of XGBoost algorithm are all within the range of 738~753 nm.Compared with the 8 commonly used rededge parameters, the 8 first-order differential spectral bands screened by the XGBoost algorithm play a more important role in accurately estimating chlorophyll concentration. This algorithm can be used as an effective hyperspectral information mining method to estimate the chlorophyll concentration of winter wheat.