A semi-empirical optical data fusion technique for merging aerosol optical depth over China

Hui Xu, Yong Xue, J. Guang, Yingjie Li, Leiku Yang, Tingting Hou, Xingwei He, Jing Dong, Ziqiang Chen
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引用次数: 3

Abstract

MODIS and MISR are two main satellites provide aerosol observations. However, AOD products generated from these two sensors by different retrieval algorithms are inconsistent. In this paper, a semi-empirical optical fusion method was proposed to produce consistent AOD with different derived AOD datasets form MODIS and MISR. Using the semi-empirical optical algorithm, new merged AOD data sets were generated over China for 2010. We used level 2 cloud screened quality assured AERONET measurements to evaluate the merged AOD results. Our results showed that the combination of MODIS and MISR with this method could produce a more consistent, reliable AOD data with great improvement in spatial AOD coverage.
中国上空气溶胶光学深度融合的半经验光学数据融合技术
MODIS和MISR是提供气溶胶观测的两颗主要卫星。然而,这两种传感器通过不同的检索算法生成的AOD结果并不一致。本文提出了一种基于MODIS和MISR的不同AOD衍生数据集的半经验光学融合方法,以获得一致的AOD。利用半经验光学算法,对2010年中国地区的AOD数据集进行了合并。我们使用2级云筛选质量保证AERONET测量来评估合并的AOD结果。结果表明,该方法结合MODIS和MISR可获得更加一致、可靠的AOD数据,AOD空间覆盖率有较大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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