## 1 Introduction  
Chlorophyll is the primary substance used by green plants to absorb, transform and transmit light energy via photosynthesis and is associated with processes related to plant growth and senescence, photosynthetic capacity, disease, nutrition and environmental stress [1]. Therefore, the evaluation of chlorophyll content is of great significance in the study of plant physiology and ecology, as it is a measure of photosynthetic capacity, nitrogen levels and developmental status. The nitrogen (N) status of plant leaves is closely related to chlorophyll content; studies show that when plant N levels are high, plant growth tends to be strong and chlorophyll content increases [2]. Chlorophyll absorption provides the necessary link between remote sensing observations and canopy state variables, so canopy state variables are used as indicators of plant N status and photosynthesis [3]. The main methods used in determining chlorophyll content are ultraviolet spectrophotometry, fluorescence analysis and chlorophyll in vivo, all of which are not only time-consuming but may also damage the studied vegetation. In contrast, hyperspectral technology, now a developed and mature technology that is increasingly being widely used in crop monitoring, carries advantages of low consumption, velocity and no vegetation damage, it therefore provides new opportunities to obtain plant physiological information [4; 5; 6]. Hyperspectral spectrometry predicts chlorophyll content by measuring the reflectance of plant leaves. The response of leaf and canopy spectral reflectance or transmittance to photosynthetic pigments can be used as a powerful means to monitor crop growth, regulate fertilizer application and estimate expected yield. The hyperspectral inversion model is established by processing the emissivity by applying spectral differentiation technology and statistical analysis technology; exploring the relationship between hyperspectral reflectance and its various variations and peanut chlorophyll by curve fitting analysis. Hyperspectral inversion of chlorophyll content was first carried out at the leaf scale and then developed at the canopy scale. Two methods are generally employed for estimating vegetation physiological parameters using hyperspectral data: (i) Optical radiation transmission model [7; 8]; and (ii) determining the empirical relationship between vegetation physiological parameters and spectral vegetation indices [9]. The spectral index method is beneficial to extract crop physiological and ecological information from remote sensing data [10], it is also important in analyzing imaging spectrometer data. Its main purpose is to enhance information contained in the spectral reflectance data by extracting changes caused by vegetation characteristics (e.g., chlorophyll and the leaf area index) while minimizing the geometric effects of soil, atmosphere and solar sensors [11], as well as to improve the utilization of spectral information and accuracy of the estimation model [12]. Spectral vegetation indices are mathematical combination of different spectral bands, mainly distributed in the visible (VIS) and near-infrared (NIR) regions of the electromagnetic spectrum [1]. It is often used to evaluate various plant leaf properties, such as the leaf area index (LAI), biomass, chlorophyll content or N content. Most current spectral indices are calculated using a ratio of two or three bands, or the normalized difference; of these, the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) are widely used in the analysis of multispectral information derived from crops, due to their simple structure and convenient calculation methodology [13; 14]. In addition, [PERSON] and [PERSON] (2001) have shown that RVI is the best method for estimating the low-density vegetation leaf area index (LAI) and canopy chlorophyll density (CCD); they propose a triangular vegetation index (TVI) sensitive to both chlorophyll content and LAI, based on the area of the triangle with vertices of green, red and NIR wavelengths.