# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 1 Introduction  
As one of the three major cereals, wheat is also the primary grain in China, making it critical to monitor the growth status of wheat [1]. As the most important pigment in the process of photosynthesis, chlorophyll is directly related to the light energy utilization of wheat. The variation in the chlorophyll index reflects the growth status of wheat at different stages and can serve for agricultural production and field activities [2]. Therefore, accurate estimation of the chlorophyll content is an essential prerequisite for high-yield and high-quality wheat production [3]. Traditional methods for measuring chlorophyll content may require manual sampling in the field combined with laboratory quantitative testing, which is time-consuming. Another limitation is that the method is not suitable for large-scale crops [4]. In recent decades, remote sensing hyperspectral technology has been widely used in crop growth monitoring, yield estimation, and pest prediction [5], and also provides a new method for estimating the chlorophyll content in crop leaves. Red light and near-infrared are sensitive to green crops in the remote sensing spectral band, which contains a large amount ofspectral information related to leaf chlorophyll content, and many scholars have carried out a lot of research. For example, as early as 1983, [PERSON] et al. [6] studied the correlation between the spectral characteristics of vegetation and chlorophyll concentration and found that the \"red edge\" position of the spectrum (i.e., the wavelength value at the maximum value of the derivative spectrum of vegetation near 700 nm) has a great contribution to the estimation of vegetation chlorophyll concentration. [PERSON] (1989)[7] used data to establish a regression equation between the location of the red edge and the chlorophyll content: \(\text{CHL}=0.05\times\text{REP}-32.1\). [PERSON] et al. [8] (2000) found that the spectral reflectance characteristics of vegetation populations at the 762 nm wavelength band and their derivative spectral data had a high correlation with chlorophyll density and developed a regression model based on the correlation. After that, [PERSON] et al. [9] proposed a calculated normalized difference vegetation index (NDVI) by processing the hyperspectral reflectance of maize leaves as a derivative and realized an efficient estimation of maize chlorophyll content based on multivariate linear regression. [PERSON] et al. [10] built a partial least squares regression model to estimate the chlorophyll content in wheat leaves. The results show that remote sensing monitoring based on vegetation index (VI) has spatiotemporal continuity, which is convenient for long-term monitoring of crop growth information and has a high application value in chlorophyll estimation [11]. [PERSON] et al. [12] studied the response characteristics of leaf chlorophyll content (LCC) and the vegetation index of field maize under different coverage conditions. They constructed a canopy LCC estimation model using random forest (RF) and partial least squares methods. [PERSON] et al. [13] found that a new combination of spectral indices aided in determining sensitive bands, enhanced the correlation of leaf nitrogen content, and performed reliably in the regression model of nitrogen content in flue-cured tobacco leaves. With the rapid development of machine learning algorithms, multiple linear regression (MLR) [14], partial least squares regression (PLSR) [15], random forest regression (RFR) [16], support vector machine regression (SVR) [17], and other methods have achieved good results in estimating leaf chlorophyll content. [PERSON] et al. [18] utilized continuous wavelet transform to analyze the chlorophyll content in potatoes. The results showed that the continuous wavelet transform method could effectively extract the wavelet characteristics that were sensitive to the chlorophyll content in potatoes and enhance the correlation between the spectrum and chlorophyll content.