Accurate estimation of crop chlorophyll content using spectral information is an important part of field crop growth assessment and the basis for precise fertilization and scientific management of crops. The rice in Northeast China was taken as the research object, a new hybrid modeling method was proposed to improve the accuracy of chlorophyll estimation and model interpretability. Firstly, based on the PROSAIL model, the canopy spectra of rice was simulated, and a lookup table for chlorophyll content was established to initially inversion chlorophyll content. Then the least squares support vector machine (LSSVM) method was used to establish the error model to compensate the PROSAIL output deviation, which can compensate for the error caused by PROSAIL modeling. To verify the proposed model’s ability to estimate, totally 13 vegetation indices that were more closely related to crop chlorophyll was selected, and then the four optimal vegetation indices were screened out through the simulation analysis of different statistical models, and the optimal prediction model for single factor input was established, including power model for GNDVI, RSI, (SDr-SDb)/(SDr+SDb), exponent model for MCARI. In addition, combined with the four vegetation indexes as input, the multi-factor prediction model of rice chlorophyll content was constructed by using partial least square method (PLS), LSSVM, BP neural network and the proposed hybrid modeling method, and the predictive model was estimated and verified. The results showed that the hybrid model had a large advantage and a low prediction bias than the optimal prediction model with single factor input. The R2 of modeling set was 0.7406, the root mean square error (RMSE) was 0.9852mg/dm2;and the R2 of verification model was 0.7332, RMSE was 1.0843mg/dm2. Compared with other multi-factor prediction models, the proposed method also had certain advantages, with high estimation accuracy and good robustness. In addition, the hybrid modeling method was based on the PROSAIL model, which the physical meaning was clear and the interpretability of the prediction model was improved. Therefore, the proposed modeling method can provide ideas and methods for chlorophyll content inversion, and provide reference for the diagnosis of rice nitrogen and monitoring of rice growth.