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In order to realize the integrated monitoring of winter wheat crop-soil total nitrogen content, a winter wheat crop-soil common canopy hyperspectral feature wavelength selection method was proposed based on improved grey wolf optimization algorithm (IGWO). Totally 40 winter wheat fields at nodulation stage in Luohe City, Henan Province were used as the study area, and the improved grey wolf algorithm was used to select the winter wheat common crop-soil feature wavelengths by collecting wheat canopy reflectance spectra and combining with precise total nitrogen values measured in the laboratory. The results showed that the improved grey wolf optimization algorithm can select the common winter wheat crop-soil canopy reflectance spectra feature wavelengths compared with other bionomics optimization algorithms such as genetic algorithm (GA). Under the random forest (RF) regression model, the coefficients of determination (R 2) of the crop and soil test sets were 0.7888 and 0.7534, respectively. Compared with other bionomics algorithms, the IGWO selected the feature wavelengths of 405nm, 495nm, 582nm, 731nm and 808nm had the best prediction performance, these feature wavelengths can effectively use the full spectrum information and meet the physiological characteristics of winter wheat. The improved grey wolf optimization algorithm proposed can select the feature wavelengths of winter wheat crop-soil common canopy reflectance spectra to achieve a higher accuracy estimation of winter wheat crop-soil total nitrogen which can be an effective way to estimate winter wheat crop-soil total nitrogen content in the field.