Unlike panchromatic and multispectral images, hyperspectral images have a high spectral resolution that causes more difficult atmospheric correction. Additionally, the atmospheric correction methods and correction accuracy based on the 6S model have been catching attention. China has launched several hyperspectral imaging sensor payloads, such as Environment 1 satellite, Gaofen 5, and resource satellites. Meanwhile, these satellites play an increasingly important position in agricultural remote sensing, forestry remote sensing, environmental monitoring and other fields, and the atmospheric correction technology for these hyperspectral remote sensing satellites is constantly developing. Taking China's hyperspectral remote sensing satellite Environment 1 satellite as an example, we carry out an optimized 6S atmospheric correction method combined with NCEP reanalysis data to improve 6S atmospheric correction accuracy.

First, considering the lack of standard reflectance products for hyperspectral images, the hyperspectral reflectance curve is constructed by the optimization estimation method and is regarded as the standard curve to verify the atmospheric correction results. Secondly, based on 6S atmospheric correction theory, we carry out sensitivity analysis and determine the most sensitive factors of aerosol optical thickness and the sensitivity of the aerosol model, atmospheric model, and atmospheric temperature and humidity to atmospheric correction coefficient. On this basis, an optimized 6S atmospheric correction method combined with NCEP reanalysis data is proposed. The aerosol optical thickness at 550 nm, atmospheric temperature and humidity profiles, and other data provided by NCEP are adopted to optimize the input parameters of the 6S model. Meanwhile, accurate atmospheric correction coefficients X_a, X_b, and X_c can be obtained, and the reflectance spectral curves of different ground objects are thus obtained after optimized atmospheric correction. Finally, by choosing Xi'an as the test area, the spectral curve of the water body is compared, and the accuracy of the correction results is evaluated via the standard curve.

The reflectance results by the 6S model and NCEP are significantly better than those by the 6S model. Compared with the standard curve, they have the same trend in spectral reflectance, and the correlation coefficient between them can reach 0.8596 with a standard deviation lower than 0.0685 (Fig. 13). The average and standard deviations of pixel-by-pixel error of ground reflectance in each band are close to 0.02, which demonstrates that the optimized 6S model with NCEP data has obvious improvement on the atmospheric correction.

The absolute error of the reflectance curve and the standard curve obtained by the atmospheric correction of the 6S model optimized by the NCEP data is much lower than that of the 6S model, and the average absolute error of each band is also less than that of the 6S model. The correlation coefficients of the three characteristic bands are higher than 0.85, the standard deviation is less than 0.07, and the mean and standard deviations of the ground reflectance per pixel error in each band are close to 0.02. Additionally, the determination coefficient between the ground object reflectance curve and the standard curve obtained by 6S+NCEP data reaches 0.78, which is higher than that by the 6S model. Meanwhile, the spectral angle of the optimized 6S model is reduced by 2.3565 and less than that of the 6S model, which indicates that the corrected spectral curve of the optimized 6S model is closer to the standard data. In conclusion, the atmospheric correction method in the 6S model of NCEP-assisted data optimization for HSI hyperspectral images can effectively improve the atmospheric correction effect.