Inversion Study of Maize Leaf Physiological Information Under Light–Temperature Stress Using Visible–Near Infrared Spectroscopy

Light and temperature are important environmental factors for maize growth. Appropriate light and temperature conditions can enhance the net photosynthetic rate of maize leaves, which in turn promotes high-quality and high-yield maize production. This study focuses on maize during the seedling stage and investigates the response patterns of maize leaf net photosynthetic rate, relative chlorophyll content (SPAD value), and visible–near-infrared spectral data under eight light–temperature stress conditions. Spectrally sensitive bands and characteristic wavelengths were extracted, and six different combinations of spectral preprocessing and modeling methods were used to establish inversion models for SPAD and net photosynthetic rate, respectively. Six different spectral preprocessing and modeling combination methods were employed to establish inversion models for the SPAD values and net photosynthetic rate of maize leaves. The research results use the correlation coefficients Rc and Rp, as well as the root mean square errors RMSEC and RMSEP, from the training and validation sets as evaluation metrics for the maize leaf physiological information inversion models. The optimal spectral combination method for the SPAD value inversion model of maize leaves was identified as PLS-MSC-SG, with an Rc value of 0.996 and an Rp value of 0.9743. The optimal combination method for the net photosynthetic rate inversion model was PLS-SNV-SG, with corresponding Rc and Rp values of 0.9848 and 0.9743, respectively. The results deepen the understanding of maize photosynthetic spectral physiological responses to light–temperature environments and provide important guidance for the precise management of maize growth conditions.