基于水体光学分类的内陆湖泊水体大气校正研究

Aiming at the scientific problem that there is not universal atmospheric correction method for the inland lakes. In this study, with high turbidity (Lake Chagan, Lake Xingkai, and Lake Taihu) and clear (Lake Hulun and Nierji Reservoir) lakes selected as the study area, we try to improve the atmospheric correction method for inland lakes by using the water optical classification method. Through the construction of inland water classification system that is suitable for the atmospheric correction, each water type can find the stable and reliable parametric model of the near-infrared backscattering coefficient. Based on this, we can build a radiative transfer optimization algorithm for the atmospheric correction of inland lakes. We hope we can break through the limitations of the existing atmospheric correction algorithm that only can be applied to special regions. Then the atmospheric correction method can provide reliable satellite data source for the inversion of water quality parameters of national or regional macro-scale “lake group".

针对内陆湖泊水体大气校正算法普适性较差这一十分关键而又尚未得到合理解决的科学问题，本研究选择高浊度（查干湖、兴凯湖和太湖）和较清澈（呼伦湖和尼尔基水库）湖泊水体作为研究区，将水体分类的思想引入内陆湖泊水体大气校正领域。通过构建适用于大气校正的内陆湖泊水体分类体系，为每类水体寻求稳定可靠的近红外后向散射系数参数化模型，实现离水信号的准确表征。在此基础上，构建基于水-气辐射传输优化的内陆湖泊水体大气校正模型，突破现有大气校正算法只能适用于特定水域的局限性，为利用遥感技术进行全国或者地区等宏观尺度“湖泊群”水质参数的反演提供可靠的卫星数据源。

OLCI作为MERIS的后继星，具有较高的时间分辨率（1-2天），和光谱分辨率（21个波段），是一种非常适合浑浊内陆水体叶绿素、藻蓝蛋白、悬浮物等水质参数估算的传感器。但是目前仍然缺乏有效的内陆浑浊水体大气校正算法。本研究提出了一种近红外亮像元与短波红外暗像元相结合的大气校正算法。该方法使用Ruddick06的近红外遥感反射率参数化模型进行近红外波段遥感反射率的参数化，使用短波红外暗像元法进行气溶胶模型的判定。通过以上两种方法的结合，提高了OLCI在内陆浑浊水体的大气校正精度。大量的野外实测验证数据表明，该方法是一种可以用于宏观尺度大范围水体反射率产品生产的大气校正方法。