The vegetation indices based on fixed characteristic wavelengths cannot be applied to the diagnosis of chlorophyll content across multiple growth stages. To solve this issue, this study proposed a diagnostic parameter based on spectral coverage area, which can be applied in multiple growth stages. The canopy reflectance spectra of 325～1 075 nm and leaf samples were collected at jointing stage, booting stage and flowering stage. The spectral were pretreated by wavelet denoising and multiple scattering correction (MSC) method and the chlorophyll content was measured by spectrophotometry. The migration range of characteristic wavelengths across different growth stages was determined by correlation analysis and a spectral parameter, named Modified Normalized Area Over reflectance Curve (MNAOC), was proposed based on the migration range coverage area. Firstly, the orthogonal experiment of wavelet parameters was designed for selecting the optimal parameters combination of wavelet basis function, decomposition layer number, threshold selection rule and threshold adjustment scheme. By the comprehensive evaluation of the SNR and S, the best parameter set was (“sqtwolog”, “mln”, “3”, “db5”). Then, correlation analysis showed that the migration range was (700 nm, 723 nm) within the characteristic wavelengths across different growth stages. After the resolution analysis, linear regression models were established for chlorophyll content diagnosis by the MNAOC with the concentration of 0.5 mg·L-1. Among them, R2c of the models were 0.840 1, 0.865 5 and 0.833 8 for each stage respectively, and R2v of the models were 0.823 7, 0.817 4 and 0.807 6 for each stage respectively. Finally, compared with the dual-wavelength based vegetation indices, the applicability advantage of MNAOC across multiple growth stages was verified. The comparison showed that MNAOC calculated by 700 and 723 nm, which contained the spectral dynamic migration characteristics, was superior to other dual-wavelength based vegetation indices, such as Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI), in terms of model accuracy and universality in multiple growth periods. The results provided support for diagnosing chlorophyll content during the growth of winter wheat in field environment.