Remote Sensing (Nov 2024)
Unlocking the Secrets of Corn: Physiological Responses and Rapid Forecasting in Varied Drought Stress Environments
Abstract
Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies.
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