Abstract: Abstract: Nitrogen has significant effect on the growth and development in crop, the formation of yield and quality. Precision diagnosis and dynamic regulation of crop is the important content and scientific basis of precision agriculture. Thus, predicting crop N status accurately and applying appropriate rate N to crop are the focus for many studies in agricultural sciences. The crop canopy nitrogen status estimation based on spectroscopy is important tool for crop nitrogen management, but its accuracy of estimation is often affected by other factors such as canopy structures.The nitrogen nutrition index (NNI) was sensitive to nitrogen status because it combined the information of plant nitrogen content, the individual character of crop and biomass, and the group character of crop. The traditional methods for calculation of plant N concentration and aboveground biomass are done manually and they are time consuming. Thus, it is hard to apply NNI in precise farming.Recently, it has been documented remote sensing technology can be used to assess many biophysical and biochemical variable of crops, especially through spectral indices. NNI was considered a good indicator of crop nitrogen status and provided new opportunities for hyperspectral applications. The objective of this study was on the NNI estimation through remote sensing spectral parameters sensitive to leaf N content and canopy nitrogen density (CND). Based on the ?eld experiments of different N rates and varieties of winter wheat from booting to filling stages, the relationships between spectral indices and leaf N and CND status in wheat were analyzed to determine the key spectral indices for assessment of leaf N content and CND. These relationships can help accurately quantitative diagnosis of nitrogen status, and provide the reference for the estimation of fertilizer rate and crop yield and quality. Upon the analysis the empirical model for NNI estimation based on the optimal parameters of leaf N and CND was established and evaluated. The results showed that, Red edge position based on linear interpolation method (REPLI), modified red edge simple ratio index (mSR705), ratio index-1dB (RI-1dB), simple ratio pigment index (SRPI), Vogelman red edge index (VOG) and other indicators had a good correlation with winter wheat nitrogen nutrition (r≥0.85), and this correlation was not affected by growing period. Therefore, they can be used to evaluate the nutritional status of canopy nitrogen inversion. Then the optimal spectral parameters were selected and the nitrogen index regression models were established. Independent experimental data was used for model validation. The results showed that REPLI in the nitrogen nutrition index estimation performed better (r=0.927, p<0.01), and the model estimation accuracy was high (R2=0.859, RMSE=0.078). Our research indicated that NNI had advantages in the field crop nitrogen nutrition diagnosis, and it had potential in qualitative and quantitative diagnosis of nitrogen nutrition status by hyperspectral inversion.