Chlorophyll is an important pigment in vegetation photosynthesis, and the traditional laboratory method needs destructive sampling and complex operation. By constructing a high-precision SPAD spectral estimation model, the real-time non-destructive monitoring of chlorophyll content in rice leaves can be realized. In this paper, the data of five key stages of transplanting, tillering stage, jointing stage, booting stage and heading stage were obtained from rice under different nitrogen levels in Heilongjiang Province. The reflectance spectrum data of rice leaves were measured by SVC HR768i spectral radiometer with a spectral detection range of 350~2 500 nm. The spectrum of the blade was measured directly by the handheld blade spectrum detector with its own light source, which was built-in halogen lamp. The SPAD value of rice leaves was measured synchronously by SPAD-502 hand-held chlorophyll meter. Leaf water is the basic raw material of plant photosynthesis, and the decrease of leaf water content will affect the normal photosynthesis of plant, resulting in the decrease of chlorophyll content and the indirect effect of water content on chlorophyll content. Therefore, the chlorophyll sensitive band and the range of water absorption are combined as the input of SPAD. The Random Forest model is an algorithm based on multiple classification trees. In the process of sampling, the algorithm includes two completely random processes, of which one is that the sampling process is carried out with a return sampling, and the other is that the sample may be repeated, and the other is random when we select the independent variables. In this paper, the spectral reflectance of rice leaves is extracted by continuum removal (CR), and the characteristic parameters of reflectance spectrum and vegetation index of rice leaves are extracted by taking into account the visible and near infrared bands. The correlation between spectral indices and SPAD was analyzed and the SPAD hyperspectral estimation model with different inputs was constructed by the Random Forests. Results are: (1) The correlation coefficient between SPAD and spectral reflectance of rice leaves was above 0.75 in the range of chlorophyll sensitive band (600～690 nm), red edge region (720~760 nm) and water absorption band (1 400~1 490, 1 900～1 980 nm). (2) In the correlation analysis between spectral parameters and SPAD, the correlation between, NDVI, DP2 and SPAD value of rice leaves was the best, and the correlation coefficients were 0.811 and 0.808; (3) The Random Forests model with CR(V1, V2, V3, V4) combined with water spectral information had the highest accuracy and R2 was 0.715, RMSE was 2.646, which could be used as a chlorophyll prediction model for rice leaves. The results revealed the spectral response mechanism of different varieties of rice, provided a high precision inversion method of SPAD values of rice leaves, and provided technical support for monitoring and regulating the normal growth process of rice in Northeast China.