# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 2 Materials and Methods  
### Study Area and Data  
As depicted in Figure 1, the experimental data comprise hyperspectral canopy measurements of wheat, collected in mid-May 2020 at the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences (located at 113\({}^{\circ}\)51\({}^{\prime}\) E, 35\({}^{\circ}\)18\({}^{\prime}\) N). The study site benefits from a favorable agricultural climate, characterized by synchronized sunlight, temperature and water resources, making it conducive to wheat growth. The experimental plots (a total of 30 plots) were randomly designed [23; 24; 25; 26], ensuring uniform management practices for wheat growth. Particular attention was given to disease and pest control, as well as weed removal. ASD FieldSpec4 is a portable spectral scanning hyperspectral imager from ASD (Analytical Spectral Devices, Longmont, CO, USA), which uses spectral scanning hyperspectral imaging technology to obtain hyperspectral information by scanning a light source such as a laser or optical fiber point by point in one dimension. ASD FieldSpec4 performance indicators are shown in Table 1 ASDFieldSpec4 was used to collect hyperspectral data from the wheat canopy during the filling phase. The spectral range covered an effective range of 350-2500 nm, with sampling intervals of 1.4 nm (350-1000 nm) and 2 nm (1001-2500 nm), subsequently resampled at 1 nm intervals. The spectrometer had a field of view angle of 25\({}^{\circ}\). Data acquisition was carried out under clear-sky conditions with favorable lighting, typically between 10:00 a.m. and 2:00 p.m. local time. Data acquisition on cloudy days may have led to the deterioration of spectral signal quality, unclear spectral characteristics and an increased shadow effect, which will affect the accuracy of the data. Therefore, data  
Figure 1: Location of the study area. acquisition was avoided on cloudy days. Thirty experimental plots were selected for data collection. During data acquisition, the sensor was positioned vertically, approximately 1 m above the canopy. Five points within each plot were sampled evenly, with each point measured ten times. The average of these ten measurements for each point was computed as the representative spectral reflectance value for that specific area. Following the collection of data from five plots, an instrument calibration was performed using a diffuse reflectance standard white panel. Finally, the acquired hyperspectral data underwent preprocessing using the ViewSpecPro Version 5.6 software  
\[R(\lambda)=\frac{I_{sample}(\lambda)}{I_{white\ reference}(\lambda)}\ \times 100\% \tag{1}\]  
where \(I_{sample}(\lambda)x_{i}\) is the intensity of reflected light from the sample object at a specific wavelength, \(I_{white\ reference}\ (\lambda)\) is the intensity of reflected light from the white reference object at the same wavelength. Once the wheat reached maturity, the harvest process for each plot was initiated. Wheat from each plot was harvested separately, bagged, air-dried and weighed, and the yield of wheat in each individual plot was determined. After comprehensive consideration of various factors, a group of 27 vegetation indices was selected [24; 27] in the wavelength range of 350-2500 nm to estimate wheat yield [28]. The chosen vegetation indices included the Normalized Difference Infrared Index, Enhanced Vegetation Index-2, Difference Vegetation Index-1, Green Atmospherically Resistant Index, Normalized Moisture Index, Normalized Red-Edge Spectral Index, Vegetation Dryness Index, Normalized Substance Index, Modified Red-Edge Ratio Index and Simple Ratio Index, among others. A list of the selected vegetation indices is provided in Table 3.