Predicting grain yield and protein content of winter wheat at different growth stages by hyperspectral data integrated with growth monitor index

• to propose the growth monitor index (GMI) of LAI and SPAD coupling by variation coefficient method. • GMI treated spectral parameters present a higher correlation coefficient with GY and GPC relative to raw spectral data. • The integration of GMI with models significantly improve the performance for predicting GY and GPC. Hyperspectral reflectance data can detect a great detail for predicting wheat’s grain yield (GY) and protein content (GPC). Based on different water and nitrogen rates, canopy spectral data and LAI and SPAD values were collected at four growth stages of wheat. The growth monitor index (GMI) was formulated by combining LAI and SPAD of wheat canopy with the variation coefficient method. Univariate models were constructed to predict GY and GPC in three forms, direct models predicted by spectral parameters, indirect models predicted with intermediate coefficient transmitted by GMI, and models predicted by GMI-treated spectral parameters. Multivariate models used single characteristic band and spectral reflectance indices (SRIs) of wheat canopy spectral reflectance. The results indicate that integrating GMI with canopy spectral data improves the prediction of spectral parameters for GY and GPC. The correlation coefficient between SAVI GMI and GY at the filling stage increased by 0.775 compared to SAVI; the correlation coefficient between SAVI GMI and GPC at the jointing stage is 0.645 higher than SAVI. By integrating GMI, the prediction model can explain over 90% of the variance in GY and 60% of the variance in GPC at the highest, the predictive performance is enhanced at some growth stages, maximally, R 2 of the prediction based on GNDVI GMI for GY is 0.927 increased by 128%, and R 2 of the prediction based on NDVI GMI for GPC is 0.532 increased by 130% relative to the original prediction at the filling stage. This study proposed the exploratory and experimental application of GMI to characterize canopy spectra.