Hyperspectral monitor on chlorophyll density in winter wheat under water stress

Abstract

Abstract Lack of water can lead lower chlorophyll concentrations and yields. Based on the hyperspectral reflectance of winter wheat ( Triticum aestivum L.), we analyzed the relationship between the canopy hyperspectral reflectance and the water‐stressed winter wheat. The correlation analysis (CA), partial least squares regression (PLSR), and successive progressions algorithm (SPA) were used to extract the important bands. Then the hyperspectral estimation models for chlorophyll density (ChD) by characteristic variables were established. The results showed that the reflectance in the visible regions increased gradually with an increasing water stress. In the near‐infrared (NIR) region, reflectance decreased with stress intensity. We extracted five and seven important waveband regions by CA and PLSR. Then we extracted right and nine important bands through SMLR, and 11 important bands were extracted by the method of SPA. We found that 427, 434, 749, and 814 nm contained important information about ChD of winter wheat after water stress. The model established by CA+SMLR was generally realized, whereas the ChD estimation models established by PLSR+SMLR and SPA with multiple linear regression had better performance, and the performance of the validation model was accurate and robust. The results of this study could provide theoretical basis and practical reference for accurate estimation of ChD in winter wheat after water stress.