# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 4 Discussion  
Accurately obtaining crop chlorophyll value is of great significance for crop growth monitoring and yield estimation [33]. With the rapid development of artificial intelligence technology, remote sensing inversion technology, which combines spectroscopy technology with machine learning, is likely to become an important means of chlorophyll remote sensing inversion in the future [34]. Compared with the existing hyperspectral vegetation index, the optimized spectral index is superior, which can solve the problems of low regional applicability of some conventional hyperspectral vegetation indices and \"oversaturation\" under different growth conditions. It can be seen that the band combination and model algorithms are key to restricting the prediction effect of chlorophyll. [PERSON] et al. [35] demonstrated the feasibility of inverting chlorophyll content from a hyperspectral vegetation index composed of reflectance in specific bands. [PERSON] et al. [36] used any combination of bands to construct four vegetation indices, and the chlorophyll estimation model established by the random forest method could accurately estimate the SPAD of the rice canopy. Previous studies have shown that the construction of an optimized spectral index can effectively reduce the interference of external environmental factors, accurately identify the sensitive bands of crop growth parameters, and significantly improve the monitoring accuracy and robustness of crop nitrogen, chlorophyll, and leaf area indices [37]. Therefore, in this study, preferred spectral indices and machine learning models were used to estimate the chlorophyll value of wheat leaves, which offset the noise caused by environmental changes to varying degrees, improved the accuracy of model estimation, and the constructed model had strong universality in different regions. The results of this study showed that the optimization of the spectral index could improve the correlation between vegetation index and chlorophyll content. Through the correlation analysis between wheat spectral index and chlorophyll content at different growth stages, it was found that the optimal vegetation index of the correlation between wheat vegetation index and chlorophyll at flag-picking stage, flowering stage, grain-filling stage, and maturity stage had NDVI. Similar to the results of [PERSON]'s [38] study on the estimation of chlorophyll in maize, indicating that the NDVI vegetation index could better reflect the growth status of crops. The optimal sensitive bands of NDVI in the four growth periods are quite different from the previously proposed NDVI (680, 800 nm) spectral sensitive bands. The may be due to the fact that the original NDVI (680, 800 nm) is based on the chlorophyll content in maize, while wheat and maize have significant differences in growth period [39], so crop type and growth period will affect the estimation ability of spectral index. Therefore, it is of great significance to optimize the bands for specific crops to figure out the best sensitive bands to improve the estimation of chlorophyll content. At present, many studies have been conducted on chlorophyll estimation, but the estimation results of different studies are quite different and uncertain. Combined with the scatter diagram, it is found that there are many underestimated points in this study. They may be related to the selection of sampling location and local terrain differences. Overall, the uncertainty of wheat chlorophyll estimation is quite large, which may be mainly due to differences in chlorophyll measurement data. The chlorophyll value is the basic datum used for estimation, and the number of samples and the quality of samples directly affect the accuracy of chlorophyll estimation. Sample points with regional representativeness can improve the accuracy of estimation, and the differences in sampling technology, sampling process, and experimental measurement process will also lead to different data measurement results [40].