Fusing cross-source spectral data and component-driven analysis for maize grain quality detection

The use of hyperspectral remote sensing technology to monitor crop water status and grain yield is important for regulating crop growth, optimizing water management and improving yield formation. Zhenghong 505 was selected as the maize variety in this study, to analyze the quantitative relationship between canopy water content (CWC) and grain yield of maize at jointing stage (V6), tasseling stage (VT), and filling stage (R2), four drought stress treatments (well-watered, mild, intermediate and severe drought) were conducted in the experimental fields of Ya’an and Renshou in Sichuan Province from 2018 to 2019. The spectral reflectance data were processed using vegetation indices and continuous wavelet transform, and a linear regression method was used to construct a quantitative CWC inversion model to explore the effectiveness of CWC as a bridge to establish a spectral inversion model for maize grain yield estimation. The results showed that the CWC estimation models using wavelet features was better than that of vegetation indices, and the linear regression models constructed with wavelet features gaus3770,64, rbio3.31635,2 and rbio3.3838,2 at the V6, VT, and R2 stages had high test accuracy with the R2 of 0.770, 0.291, and 0.233, respectively. The linear regression models established between CWC and maize grain yield all reached highly significant levels (P < 0.01), with R2 of 0.596, 0.366 and 0.439 at the V6, VT, and R2 stages, respectively. The yield prediction model based on the basis of spectral reflectance was the best validated with the wavelet feature gaus3770,64 (R2 = 0.577, RMSE = 1.625 t hm-2) at V6 stage, which can be used as the best period for predicting maize grain yield. Therefore, the “spectral reflectance-canopy water content-yield” modeling method proposed in this study can achieve an accurate estimation of maize grain yield and provide a theoretical basis for future large-scale monitoring of maize productivity.