Abstract: Chlorophyll is one of the most important photosynthesis pigments in crops. The photosynthetic capacity of the plant can also indicate the plant's growth and nutritional status. Leaf chlorophyll content can be accurately acquired to monitor crop growth and yield. The portable optical instrument SPAD-502 can be used to rapidly acquire the SPAD value in a non-destructive way, indicating the leaf's relative chlorophyll content. However, the tremendous amount of hand labor cannot fully meet the needs to estimate the leaf SPAD value in a large area and then monitor the dynamic of crop growth and agricultural management. Alternatively, optical remote sensing can be expected to non-destructively measure the leaf SPAD value at a large scale. This study aims to evaluate the effects of spectral resolution on the optical SPAD estimation of winter wheat leaf using remote sensing. A four-year field experiment was carried out under four growth stages (jointing, heading, anthesis, and filling) and three levels of nitrogen application. A systematic investigation was implemented to determine the canopy spectral reflectance and leaf SPAD values of winter wheat. The leaf SPAD estimation model was constructed using 25 commonly used chlorophyll content-sensitive spectral indices combined with machine learning. An evaluation was also made on the effects of five spectral resolutions on the reflectance of a single band, spectral indices, and the estimation of SPAD value using machine learning. The results show that the sensitivity of single-band reflectance to SPAD was dominated by the spectral resolution, growth stages, and nitrogen application levels. In the whole growth period, the reflectance of the red band was more sensitive to the SPAD than the rest bands, where the determination coefficient R² was between 0.411 and 0.579 at the five spectral resolutions. The reason was that there was a strong absorption of chlorophyll in the red band. Specifically, the R² was between 0.242 and 0.700, and the Var was between 0.313 and 0.952 at the most sensitive spectral resolution for the red band in each growth stage. The red edge band reflectance at 710 nm shared the largest variation coefficient of spectral resolution sensitivity, and Var was 1.000 in the whole growth period. The main reason was that the reflectance of green vegetation changed sharply in the red edge band, indicating outstanding differences in the reflectance at different spectral resolutions. The spectral resolution also dominated the sensitivity of spectral indices to SPAD at different growth stages and nitrogen application levels. Except for the filling and anthesis stage, the spectral resolution range of the optimal sensitivity spectral index was between 25 and 50 nm. The potential reason was that the broad spectral resolution caused the spectral index to contain more spectral information, and then reduce the influence of noises, particularly for the optimal sensitivity spectral index. However, there were limited effects of spectral resolution on the sensitivity of the optimal spectral index to SPAD, compared with the single band reflectance. In the whole growth period, the spectral index mND705 presented the strongest sensitivity to SPAD at 50 nm (R² = 0.685) and a low variation coefficient of spectral resolution sensitivity (Var = 0.014). In each growth stage, the R² of the most sensitive spectral index to SPAD was between 0.387 and 0.895, and the coefficient of sensitivity variance Var was between 0.01 and 0.05. The nitrogen application level enhanced the leaf chlorophyll content to improve the sensitivity of the spectral index to detect SPAD. Spectral resolution of spectral indices was optimized as the feature inputs for machine learning models, in order to improve the estimation accuracy of SPAD. In the whole growth period, the best accuracy of estimation was achieved in the model with 25 nm spectral resolution spectral index and PLSR, indicating the R² of 0.816 and RMSE of 4.04. In each growth period, the model with the optimized spectral resolution spectral index as the input also shared the best estimation accuracy with R² between 0.531 and 0.916 and the RMSE between 2.76 and 4.47. At the three levels of nitrogen application, the R² was between 0.780 and 0.884 and the RMSE was between 2.67 and 4.55, according to the model with the optimized spectral resolution. The nitrogen application level improved the chlorophyll content of leaves. Thus the detection of SPAD was enhanced in the machine learning model using spectral characteristics. The spectral index and the spectral resolution were optimized to improve the estimation accuracy of winter wheat leaf SPAD using optical remote sensing. The finding can provide theoretical support to design the optical remote sensing sensor.