HYPERSPECTRAL MONITORING OF PLANT WATER CONTENT IN WINTER WHEAT (TRITICUMAESTIVUM L.) AFTER DROUGHT STRESS

Winter wheat (Triticum aestivum L.) is an important food crop with high economic and social value. Frequent drought disasters in northern China, have a negative influence on the growth and development of winter wheat, thus the risk of diseases and pests is increased, and yield and quality are reduced. We took winter wheat treated with different water stress as the research object, collected the plant water content (PWC) at different growth stages and hyperspectral remote sensing data, and carried out logarithmic (Lg R), first derivative (R') and logarithmic first derivative (Lg R') transformation of the raw spectrum (R) of winter wheat. We established the spectral prediction model of PWC by analyzing the quantitative relationship between the spectral data of winter wheat under different mathematical transformations and the PWC in the range of 350-2500 nm. The results showed that the prediction model of PWC based on lg R' had the highest accuracy, which can provide an effective basis for rapid and non-destructive monitoring of winter wheat moisture.