Monitoring model of leaf area index of winter wheat based on hyperspectral reflectance at different growth stages

Hyper-spectral remote sensing can rapidly and non-destructively acquire vegetation canopy information. It is an important real time technology to monitor and manage crop growth. Leaf area index (LAI) is a key parameter for crop growth evaluation and yield prediction. The objectives of this study were to establish wheat LAI estimation model based on winter wheat (Triticum aestivum L.) canopy hyper-spectral reflectance with different rates of nitrogen or phosphorus application, and to improve the forecast precision of the LAI estimation model at different growth stages of winter wheat on the Loess Plateau of China. The experiments were carried out during 2009-2014 at Northwest A&F University, Yangling, China. The results showed that LAI of wheat was increased with increase in nitrogen and phosphorus application rate at different growth stages, and LAI from jointing to maturity showed a parabolic curve, and the maximum LAI of wheat was at heading stage. This result provides technical support for growth monitorin