Nitrogen (N) is one of the most important nutrient matters for crop growth and has the marked influence on the ultimate formation of yield and quality in crop production. As the most mobile nutrient constituent, N always transfers from the bottom to top leaves under N stress condition. Vertical gradient changes of leaf N concentration are a general feature in canopies of crops. Hence, it is significant to effectively acquire vertical N information for optimizing N fertilization managements. Especially, if crop growth status in the middle and bottom leaf layers can be detected early, optimal N utilization might come true. So, it is critical to obtain N vertical information especially in lower leaf layers for accurate evaluation of N nutrient status in winter wheat and timely N recommendation. The rapid detection of vertical N information is difficult by conventional ways. Spectral detecting, especially hyperspectral remote sensing with hundreds of very narrow spectral bands plays a unique role in determining crop biochemical parameters including N status thanks to the notable characteristics of non-destruction and quickness. This technique contributes to the development of fertilizer recommendations and is likely to avoid both the environmental pollution of excessive N fertilization and the effects of N-deficiency in crops. The paper aimed at the relationships between canopy hyperspectral reflectance and N estimation at the different layers of winter wheat canopy and developed a method to estimate N status in different leaf layers of winter wheat canopies. This study used the field data from anthesis of winter wheat in 2016 and coupled a mathematical algorithm, OWC (optimal weight combination) with hyperspectral reflectance to estimate N concentration in different leaf layers of winter wheat. The results showed that OWC yielded $\mathrm{R}^{2}$ values of 0.45, 0.59, 0.25, 0.43 for the first, second, third and fourth leaf from top to bottom in wheat canopies, respecti...