The researchers studied how to use hyperspectral remote sensing to monitor leaf dry weight and leaf area index in wheat. They conducted three experiments with different wheat varieties and nitrogen levels over three growing seasons. They found that leaf dry weight and leaf area index increased with higher nitrogen levels and showed distinct patterns during growth. The best spectral bands for monitoring were in the red light and near infrared regions. By analyzing vegetation indices, they developed accurate models to estimate changes in leaf dry weight and leaf area index across different growth stages and nitrogen levels.