Accurate and non-destructive estimation on the growth status of winter wheat is of significance. The consecutive two-years experiments of nitrogen application in 2011—2012 and 2012—2013 were performed to obtain the canopy spectra and the six growth status indicators of winter wheat (Leaf area index, LAI; Above ground dry biomass, AGDB; Above ground fresh biomass, AGFB; Plant water content, PWC; Chlorophyll density, CH.D; Accumulated nitrogen content, ANC). The principle component analysis (PCA) was implemented to construct the comprehensive growth indicator (CGI), which could potentially represent the growth status of winter wheat. Furthermore, the method of partial least square (PLSR) was applied on constructing the hyperspectral prediction models of all growth indicators and validating the accuracy of CGI. The results showed that the constructed CGI significantly correlated with all the growth status indicators of winter wheat, excepting for the PWC. It indicated that the CGI could represent most of the information for the six indicators and the CGI also could be used to stand for the growth status of winter wheat. Moreover, the model performance of CGI and other six indicators were further compared, and it showed that the PLSR model of CGI performed best than other six indicators with R2=0802, RMSE=1268, RPD=2015. The CGI model was validated and proved to be more accurate and robust (R2=0672, RMSE=1732 and RPD=1489). The study showed that the CGI constructed with the PCA method could represent the growth status of winter wheat and the CGI model based on the PLSR method could be used to estimate the growth status of winter wheat. It also indicated that the multivariate statistical analysis had great potential to be applied in the field of crops by using the hyperspectral technology.