# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 4 Discussion  
This study aims to improve the accuracy and robustness of crop yield prediction models. In this study, the data of two planting seasons from 2016 to 2018 were used to evaluate the improvement effect of GY prediction model. The information from multiple cropping seasons increased the variability of the data (Tables 2 and 3), which was due to the differences in environmental and phenological conditions between different years and enhanced the prediction accuracy and transferability of the model. In recent years, hyperspectral remote sensing has great potential for application in rapid and accurate estimation of crop parameters and yields in agricultural fields because of its advantages of narrow waveband and strong continuity [69]. Considering the information overlap between adjacent bands, 15 vegetation indices were calculated in this study for constructing the GY prediction model instead of all bands in order to avoid generating data redundancy. These indices are located in the visible red and near-infrared wavelengths, which are important for photosynthesis in plants and are often used for timely monitoring of vegetation cover and growth vigor, etc. [70]. The results of the correlation analysis (Figure 4) indicated that all vegetation indices performed a highly significant correlation with GY (\(p<0.001\)). However, there are differences in the potential of different vegetation indices for GY prediction due to the fact that the vegetation information obtained from different spectral channels has various correlations with different elements of vegetation or a certain character state. The NDWI index showed a high correlation with GY due to its ability to effectively extract the water content of the vegetation canopy and respond to moisture changes in a timely manner [71]. The VREI index provides a timely response to changes in chlorophyll concentration, canopy structure and water content in plants, and is often used in vegetation phenology change monitoring, precision agriculture and vegetation productivity modeling [59]. The vegetation  
Figure 7: The distribution of regression coefficients for each growth stage within the level-2 model (ENET). (**a**) Without LAI; (**b**) With LAI. tion cover will change as the fertility period advances, and the effect of soil background can be effectively reduced by introducing MCARI and SAVI. The ARVI can effectively reduce the dependence of spectral reflectance on atmospheric properties [48], which is important for the transfer of models to airborne remote sensing platforms. NDVI eliminates most of the variation in irradiance associated with instrument calibration, sun angle, topography, cloud shadows, and atmospheric conditions, enhancing the response to vegetation [52]. PRI is sensitive to changes in carotenoid content in plants and gradually increases in correlation with GY during late crop growth. SIPI and PSRI indices are often used for canopy physiological stress monitoring and crop yield analysis [56; 58]. In addition, the correlation between the same vegetation index and GY varies at different growth stages. This is due to the fact that as the crop fertility progresses, the growth and development of the plant and the influence of the external environment led to changes in its internal components and structure, which cause the spectral characteristics of the plant canopy or leaves to change subsequently [72]. Thus, the combination of multiple vegetation indices used to construct the GY prediction model can achieve complementary information to overcome the limitations of a single band or a single index, which effectively improves the prediction accuracy and robustness of the model (Table A7). This has important significance for the transfer of the model to practical applications. The essence of yield formation is the complex process of production, transportation, accumulation, and distribution of photosynthetic products [73]. As the reproductive period advances, spectral saturation of the vegetation index occurs when the vegetation cover is greater than 80%.