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$addC of spectral reflectance were extracted. The sensitive bands for the leaf chlorophyll were determined by means of correlation analysis, and then the quantitative relationship between leaf chlorophyll content and canopy reflectance spectra were established. The results showed that there was a major difference in canopy reflectance spectra among different planting densities. As the density increased reflectance in the region of visible spectrum increased, while in the region of near infrared, reflectance decreased. The reflectance spectra of two cultivars showed the opposite in the region of visible and near infrared spectrum. Near infrared bands (780-1100 nm) was better than visible bands (460-730 nm) in differentiating planting densities of different wheat cultivars. The bands of 650, 670, 1200 and 1260 nm could be defined as sensitive bands to monitor leaf chlorophyll content of winter wheat. The predicting models of leaf chlorophyll content were established with the use of the variables of vegetable index (VI). The testing results showed better effect, with the average deviation of model itself lower than the average deviation of cross test, so leaf chlorophyll content could be well imitated and forecasted by canopy spectral. Overall, the monitoring models of leaf chlorophyll content showed good test results, with reliable estimation from DVI (1200, 670) of ` Le639 ` at the booting stage and PVI (1200, 730) of ` Jing9549 ` at the joining stage. Compared with single variable models, there was a higher R2, lower SE and RMSE for composite models that revealed better anticipating effect of composite models than single variable models. Therefore, it is feasible to monitor leaf chlorophyll content of winter wheat in the key growth stages using the spectral vegetation indices