MERIS数据以其更为合理的水色波段设置和300 m较高的空间分辨率,在内陆湖泊水环境遥感监测中有较大的应用潜力,对其进行有效的大气校正则是水环境参数定量化反演的前提。以太湖为研究区,研究基于氧气和水汽吸收波段的暗像元为假设条件,改进传统的近红外波段暗像元大气校正方法,采用MERIS level 2p数据辅助获取湖区气溶胶参数,并利用2007-11-11、2008-11-20以及2009-04-25等 3景MERIS影像进行验证。结果表明,该方法能够快速、有效地完成MERIS影像的大气校正,与地面准同步实测数据相比,3次校正的均方根百分比RMSP (Root Mean Square of Percentage)、都在25%以下; 与Beam自带的二类水体大气校正算法、气溶胶厚度辅助的6S大气校正以及改进的暗像元算法进行精度比较,结果表明该方法校正精度较高。由于该方法不需要同步实测气溶胶数据,因此具有一定的适用性。

A Medium-Resolution Imaging Spectrometer (MERIS) sensor, with reasonable ocean color bands and a high spatial resolution of 300 m, has considerable potential with respect to the monitoring of inland waters. In the quantitative retrieval of water environmental parameters by using a remote sensing image, accurate atmospheric correction is very significant. In the present study, oxygen and water vapor absorption bands are used for improving the traditional black pixel assumption based on the near-infrared bands. The method of calculating aerosol parameters through MERIS Level 2p data is developed; this method is assessed using MERIS Level 1p data recorded on Nov. 11, 2007; Nov. 20, 2008; and April 25, 2009. Meanwhile, the in situ measured data are used for a comparison with the modeled values. The obtained results show that the proposed method is efficacious with RMSP of less than 25%. Another important work is also carried out, that is, a comparison of the proposed method with other algorithms such as a case II atmospheric correction algorithm embedded in a MERIS (Beam) processor, aerosol-thickness-aided 6S, and modified black pixel algorithm. The results indicate that the proposed algorithm has certain applicability considering, because it is independent of the synchronous measured aerosol data.