Abstract: Abstract: Crop growth monitoring is an important research field of agricultural remote sensing. Crop growth monitoring can have timely access to crop growth information, and it is significant for crop management. The use of unmanned aerial vehicles (UAV) to monitor crop growth has become a fast and effective way. Some indicators can be used to characterize the growth of wheat including leaf area index (LAI), leaf chlorophyll content (LCC), plant nitrogen content (PNC), plant water content (PWC) and biomass of wheat. LAI and biomass were used to characterize the population growth of wheat. LCC, PNC and PWC were used to characterize the individual growth situation of wheat. This paper integrated the LAI, LCC, PNC, PWC and biomass of wheat, 5 indicators of wheat growth, into an index named comprehensive growth index (CGI) according to the equal weight for each indicator. The normalized difference spectral index (NDSI), ratio spectral index (RSI) and simple spectral index (SSI) were constructed by using single band and any 2 bands in the range of 450-882 nm. Then, the correlations between the CGI and the spectral indices were analyzed, and the spectral indices were selected based on good correlation between CGI and spectral indices i.e. NDSI, RSI, and SSI. At the same time, the spectral indices were constructed and selected by the correlation between LAI, LCC, PNC, PWC and biomass and spectral band. The inversion model was established with spectral indices and the partial least squares regression (PLSR). Four inversion models were established during jointing, booting, flowering, and grain filling period, respectively. CGI and the hyperspectral image of UAV were used to reflect the growth of wheat in different growth stages, realizing the monitoring of wheat growth during jointing, booting and flowering period. And through the hyperspectral image of UAV, the growth of wheat and difference of different growth stages of wheat can be monitored. The hyperspectral image information after inversion is extracted to verify the accuracy of the model for different growth stages. The results were as follows: 1) Some spectral indices and sensitive single band were selected for CGI in different growth stages of winter wheat. The spectral indices NDSI(746,742), RSI(746,742), SSI(526，454) and single band 690 nm were selected in wheat jointing period. The spectral indices NDSI(758,694), RSI(754,694), SSI(684,566) and single band 662 nm were selected in wheat booting stage. The spectral indices NDSI(774,758), RSI(774,758), SSI(510,486) and single band 570 nm were selected in wheat flowering period. The spectral indices NDSI(754,746), RSI(754,746), SSI(582,498) and single band 702 nm were selected in wheat filling period. 2) Overall, the coefficient of determination R2 between CGI and spectral indices was higher than that of the individual index and the corresponding spectral index in the winter wheat growth period. Only the R2 between CGI and RSI(754,694) was lower than that between RSI(486,518) and LCC in the booting period. The R2 between CGI and R570 was lower than that between biomass of wheat and R834 in flowering period. The R2 between CGI and SSI(582,498) was lower than that between PWC and SSI(790,862) in filling period. 3) The R2 values of modeling PLSR at jointing stage, booting stage, flowering stage, filling stage and full growth stage were 0.70, 0.72, 0.78, 0.78, and 0.61 respectively. For model validation, the NRMSE (standard root mean square error) values were 0.066, 0.038 and 0.062, and the RMSE (root mean square error) values were 0.050, 0.032 and 0.047, respectively, at the jointing stage, booting stage and flowering period. Overall, the R2 of the CGI and the corresponding spectral index is higher than that of each index and the corresponding spectral index. The spectrum can well reflect the information contained in CGI. The PLSR modeling and image validation accuracy are high. It is feasible to use the integrated growth index to monitor the growth of wheat, which is of high precision.