## 4 Discussion  
Hyperspectral data, in contrast to multispectral data, consist of numerous continuous bands that can be converted into derivatives. This paper demonstrates that the optimal spectral index determined by the reflectance of the first-order derivative spectra is more effective than the original spectra in estimating the nitrogen nutrient index, which is consistent with the findings of other scholars [31]. This is because the first-order derivative spectra are better at reducing the influence of soil background and other external noises.  
This paper analyzes the correlation between the nitrogen nutrition index and three spectral indices formed by combining all the bands in two combinations. Two sensors were employed to acquire spectra in the 454-830 nm range, which were then transformed  
Figure 6: Comparison of predicted and measured values of NNI in winter wheat retrieved by regression model.  
into first-order derivative spectra. Additionally, an isotropic diagram of the coefficient of determination of the nitrogen nutrition index was plotted. Finally, the band combinations corresponding to the spectral indices with the highest correlation with the nitrogen nutrition index of winter wheat were identified. The model constructed using the UHD185 spectral index demonstrated superior performance in both the modeling and validation sets when considering the 666 nm and 826 nm band combinations. The R2 values for modeling and validation were 0.85 and 0.75, respectively. These values correspond to the model equation \(y=-2.0655x+0.156\).  
Regarding the evaluation of UHD185 remote sensing data from UAVs, the results of this study are similar to those of [PERSON] et al. and [PERSON] et al. However, [PERSON] et al. argued that the NDVI and OSAVI calculated by UHD185 had no obvious deviation from those calculated by ASD from the perspective of spectral indices [32], and [PERSON] et al. evaluated the data quality of the UHD185 spectra from the perspective of the red-edge parameter and the combination of the red-edge parameter and the vegetation index. The present study evaluated the data quality of UHD185 spectra from the perspective of red-edge parameters and the combination of red-edge parameters and vegetation index [33]. In this study, we evaluated the data quality of UHD185 spectra in terms of the trend of spectral curve changes, spectral correlation, and the phenomenon of \"redshift\" under certain fertility periods.  
This paper only gathered hyperspectral images and ground truth data of winter wheat during the modulation and flowering stages, within a limited time frame. As a result, the sample size used for modeling was small, leading to lower model reliability. Additionally, the modeling was performed based on the reproductive period, which affected the development of a generalized model. To enhance the stability and generalizability of future research, it is necessary to include data from multiple years and different regions. Hence, there is a need to conduct additional research on the extended-term monitoring of crops using UAV hyperspectral technology, specifically focusing on various fertility stages.  
Based on the current study, future research will focus on how to address crop nitrogen monitoring in heterogeneous spaces under different inter-annual periods, so as to monitor crop nitrogen abundance and deficiency in farmland, guide fertilizer application, and use nitrogen monitoring models to generate fertilizer follow-up prescriptions, which will in turn improve nitrogen fertilizer utilization, increase economic benefits, and achieve double-high agriculture that simultaneously improves crop yields and resource utilization.