In response to the requirements of “One Regulatory, Two Reduction and Three Basic”, the use of chemical fertilizers was reduced without reducing production. This paper studies the SPAD value spectral estimation model with different increasing density and reducing fertilizer treatments under the condition of full mechanization and machine-transplanted rice with long seeding age and analyzes the correlation between SPAD value of rice leaves and the first derivative spectral reflectance of the canopy spectrum. The SPAD values estimation model were established based on the sensitive wave lengths and the first derivative spectra parameters. The R2, RMSE and RE were used to evaluate the accuracy of the models. The results found that the 6 treatments reached extremely significant correlations in the 634～652 nm, 655～668 nm, 497～552 nm and 687～711?nm bands, all four bands with a correlation coefficient of more than 0.700. Among the models that use the sensitive wavelength as the independent variable to estimate the SPAD value, the polynomial model of low-density fertilizer reduction had the best estimation effect, with 0.766, 1.185 and 2.152% for the calibration set R2, RMSE and RE, and 0.729, 0.364 and 0.752% for the validation set R2, RMSE and RE, respectively. Among the models that use the spectral index as the independent variable to estimate the SPAD value, the medium-density fertilizer reduction has the best estimation effect and its calibration set R2, RMSE and RE were 0.703, 1.314 and 2.359%, respectively, the validation set R2, RMSE and RE were 0.763, 0.620 and 1.198%, respectively. The study found that under the same density conditions, reducing fertilizer would reduce the SPAD value and weaken growth; the higher the transplanting density, the higher the spectral reflectance; the influence of transplanting density on the spectral reflectance of the rice canopy was greater than the fertilization level. All treatments showed that the difference spectral index (DSI) model effect was the best, indicating that the selection of sensitive wavelengths and spectral parameters is critical to the construction of the model.