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The winter wheat early growth period consists of reviving stage, early jointing stage and later joint stage, which is the most important period for precision management. It is significance to understand the growth status of winter wheat and provide accurate and scientific data for precision agriculture in the early growth period. The general model used to describe the canopy reflectance and chlorophyll of early growth period was necessary. The linear regression models, which used character wavelength as independent variable, were constructed. The PLS(partial least square) algorithm was applied to construct multiple regression models, which used the vegetation index as the independent variables. All models were used to predict the chlorophyll content in the same period and different periods. The better general model was found according to the predictive effect. According to the curve of correlation between reflectance and chlorophyll content, the curve ranged from 500 nm to 600 nm contained the extreme values. The linear regression independent variable was 550 nm selected from this range. The linear regression model of the reviving stage predicted accurately in the early jointing stage and poorly in the later jointing stage. In contrast, the multiple regression prediction model of the reviving stage had more versatile. It showed the satisfactory predication in early and later jointing stage. In order to improve the accuracy of predication in different stages, MPRI(mobil PRI) was developed to construct the multiple model with NDVI(normalized differential vegetation index) and RVI(ration vegetation index), instead of TCARI. The test results proved that MPRI was simpler than TCARI on the parameter and the structure. The multiple model, made by MPRI，NDVI and RVI was general for the winter wheat growth periods.