遥感影像的大气校正是水色参数反演的前提。以太湖为研究区，采用6S大气辐射传输模型以及近红外波段离水反射率模型相结合的方法。通过神经网络模拟大气辐射传输过程，并利用中分辨率成像光谱仪(MERIS)754、779、865、885 nm 4个近红外波段进行光谱优化，求算550 nm处气溶胶光学厚度等变量。通过外推，实现可见光波段的大气校正。将2007年11月11日、20日、21日，2008年11月20日MERIS Level 1p影像以及野外实测水体遥感反射率数据集用于精度验证，结果表明，该方法能够较好地反演水体遥感反射率光谱，校正后的13个波段的平均相对误差大多分布在20%~40%之间。与6S以及Beam 4.9软件自带的大气校正方法相比，具有较高的校正精度和较强的稳定性，在太湖有较好的适用性。

Atmospheric correction of remote sensing image is the precondition of inversing water color parameters. An atmospheric correction method for lake Taihu based on 6S the radiative transfer model and the NIR water-leaving reflectance model is proposed. Two neural networks are built to model radiative transfer, and thereafter, estimate parameters such as aerosol optical thickness in 550 nm with the optimization of four near infrared bands (i.e, 754, 779, 865 and 885 nm) of medium-resolution imaging spectrometer (MERIS) data. Then, the parameters are extrapolated to visible wavelength to fetch the parameters in those visible bands. This model by MERIS Level 1p data on November 11th, 20th, 21th, 2007 and November 20th, 2008 and the in situ date-sets is validated. The result indicates that the radiative transfer optimized model has a high precision in inversing the water remote sensing reflectance with an average relative error from 20% to 40% in the 13 bands. Comparing with the atmospheric correction methods of 6S and Beam 4.9, it has the highest correction precision and strongest stability, and is applicable in lake Taihu.