Abstract: Leaf nitrogen content is closely related to leaf photosynthesis and the nutritional status of winter wheat plants, which directly affects the plant growth and development. While the stem nitrogen content is closely related to the proportion and content of cellulose, hemicellulose and lignin in stems, which directly affects stem quality and plant lodging resistance. However, it is still lacking on the direct estimation of stem N content in winter wheat. It is very necessary to evaluate the stalk quality and predict lodging from the perspective of stem nitrogen content. In this study, a 2-year field experiment was conducted to accurately estimate the nitrogen content in different plant organs (leaves and stems) of winter wheat. Winter wheat canopy spectral reflectance, leaf and stem nitrogen content, and leaf SPAD (Soil and Plant Analyzer Development) values were obtained at four growth stages (jointing, heading, anthesis and filling) and three nitrogen application levels (N1, N2 and N3). A systematic analysis was made to determine the sensitivity of hyperspectral vegetation indices to leaf and stem nitrogen contents at different growth stages and nitrogen application levels. Five commonly-used machine learning algorithms were used to estimate the leaf and stem nitrogen contents of winter wheat, including random forest regression (RFR), support vector regression (SVR), partial least squares regression (SVR), partial least squares regression (PLSR), Gaussian process regression (GPR) and deep neural networks (DNN). The hyperspectral vegetation indices only or combined with SPAD were used as the inputs to construct the nitrogen estimation models for leaves and stems. The results showed that the sensitivity of hyperspectral vegetation indices to the nitrogen content in leaves and stems was influenced by the growth stage and nitrogen application level. In the filling stage, the best vegetation index DCNI (Double-peak canopy nitrogen index) shared the highest sensitivity to leaf nitrogen content, where the determination coefficient R² was 0.866. The sensitivity to stem nitrogen content was the highest at the heading stage, and the R² between the best vegetation index NPQI (Normalized phaeophytinization index) and nitrogen content was 0.677. The sensitivity of the spectral vegetation index to the stem nitrogen content increased with the increasing nitrogen application level. The machine learning combined with the SPAD value and vegetation indices was improved the estimation accuracy of the nitrogen content, compared with only the vegetation index. In leaf nitrogen content, the estimation accuracy increased by 1%-7% under different growth stages and nitrogen application levels. The normalized root mean square error (NRMSE) reduced from 0.254 to 0.214 during the whole growth stage. In a single growth period, the NRMSE reduced from 0.201 to 0.128 at the heading stage, indicating the most increase. In the stem nitrogen content, the NRMSE reduced from 0.443 to 0.400 during the whole growth stage. There was the most increase during the heading stage with the values ranging from 0.323 to 0.268. In the whole growth period, the DNN model combined with the SPAD value was achieved the best accuracy to estimate the nitrogen content of leaves (R²=0.782 and NRMSE=0.214) and stems (R²=0.802 and NRMSE=0.400). The combination of the SPAD value and spectral vegetation index can be expected to improve the accuracy of the nitrogen content in the leaves and stems of winter wheat at different growth stages and nitrogen application levels.