高分二号卫星的成功发射，标志着我国遥感卫星进入了亚米级高空间分辨率时代，遥感影像在定量反演，地物识别和变化分析等领域将有重要作用。大气校正的精度是影响其定量化应用的重要因素。由于高分二号遥 感数据缺乏短波红外波段，无法采用暗像元法进行大气校正。提出一种基于辐射传输模型的高分二号影像大气校正方法，利用 6S(second simulation of the satellite signal in the solar spectrum)辐射传输模型建 立大气校正系数查找表，利用同步 MODIS影像数据结合改进后的暗像元方法反演气溶胶光学厚度，确定大气校正系数，消除高分二号影像大气分子和气溶胶等的吸收和散射的影响，实现 GF-2数据的大气校正。选取地表平坦 均一的敦煌辐射校正场作为实验区，通过同步的实测数据对校正结果进行精度评价，并且比较大气校正前后归一化植被指数 NDVI。结果表明：最小相对误差仅为 0.9%，大气校正之后影像数据更真实地反映了地物的反射特 性；大气校正后的 NDVI大大增强了植被信息反差，突出了 GF-2卫星传感器的植被信息区分能力。

The successful launch of the GF-2 satellite indicates that China's remote sensing satellites have entered the era of high spatial resolution of the sub-meter level. Remote sensing images will play an important role in quantitative inversion, ground object recognition, and change analysis. The accuracy of its atmospheric correction is an important factor that affects its quantitative application. Due to the lack of a short-wave infrared band in GF-2, it is impossible to use a dark pixel method for atmospheric correction. A method of atmospheric correction for the GF-2 image based on a radiation transfer model is proposed. The atmospheric correction coefficient lookup table is established by using 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiation transfer model. The aerosol optical thickness (AOT) is retrieved by combining synchronous MODIS image data with an improved dark pixel method. The atmospheric correction parameters are determined to eliminate the influence of absorption and scattering of atmospheric molecules and aerosols in the GF-2 image and to achieve atmospheric correction of GF-2 data. Dunhuang radiation correction field with a flat and uniform surface is selected as the experimental area. The accuracy of the correction results is evaluated by synchronous measured data, and the normalized difference vegetation index (NDVI) before and after atmospheric correction is compared. The results show that the minimum relative error is only 0.9%. The image data after atmospheric correction can accurately reflect the reflection characteristics of ground objects. NDVI after atmospheric correction greatly enhances the contrast of vegetation information and highlights the ability of vegetation information discrimination of the GF-2 satellite sensor.