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利用AERONET测量评估VIIRS陆地气溶胶模型选择

Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements.

作者信息

Wang Wei, Pan Zengxin, Mao Feiyue, Gong Wei, Shen Longjiao

机构信息

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

出版信息

Int J Environ Res Public Health. 2017 Sep 5;14(9):1016. doi: 10.3390/ijerph14091016.

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. Identifying land aerosol types is important because aerosol types are a basic input in retrieving aerosol optical properties for VIIRS. The VIIRS algorithm can automatically select the optimal land aerosol model by minimizing the residual between the derived and expected spectral surface reflectance. In this study, these selected VIIRS aerosol types are evaluated using collocated aerosol types obtained from the Aerosol Robotic Network (AERONET) level 1.5 from 23 January 2013 to 28 February 2017. The spatial distribution of VIIRS aerosol types and the aerosol optical depth bias (VIIRS minus AERONET) demonstrate that misidentifying VIIRS aerosol types may lead to VIIRS retrieval being overestimated over the Eastern United States and the developed regions of East Asia, as well as underestimated over Southern Africa, India, and Northeastern China. Approximately 22.33% of VIIRS aerosol types are coincident with that of AERONET. The agreements between VIIRS and AERONET for fine non-absorbing and absorbing aerosol types are approximately 36% and 57%, respectively. However, the agreement between VIIRS and AERONET is extremely low (only 3.51%). The low agreement for coarse absorbing dust may contribute to the poor performance of VIIRS retrieval under the aerosol model ( = 0.61). Results also show that an appropriate aerosol model can improve the retrieval performance of VIIRS over land, particularly for dust type ( increases from 0.61 to 0.72).

摘要

可见红外成像辐射计套件(VIIRS)是一种下一代极轨业务环境传感器,具备全球气溶胶观测能力。识别陆地气溶胶类型很重要,因为气溶胶类型是反演VIIRS气溶胶光学特性的基本输入数据。VIIRS算法可通过最小化推导光谱与预期光谱地表反射率之间的残差,自动选择最优陆地气溶胶模型。在本研究中,利用2013年1月23日至2017年2月28日从气溶胶机器人网络(AERONET)1.5级获取的并置气溶胶类型,对这些选定的VIIRS气溶胶类型进行评估。VIIRS气溶胶类型的空间分布和气溶胶光学厚度偏差(VIIRS减去AERONET)表明,错误识别VIIRS气溶胶类型可能导致在美国东部和东亚发达地区VIIRS反演值被高估,而在南部非洲、印度和中国东北地区被低估。约22.33%的VIIRS气溶胶类型与AERONET的气溶胶类型一致。VIIRS与AERONET在细颗粒非吸收性和气溶胶类型上的一致性分别约为36%和57%。然而,VIIRS与AERONET在粗颗粒吸收性沙尘方面的一致性极低(仅为3.51%)。粗颗粒吸收性沙尘的低一致性可能导致在气溶胶模型下VIIRS反演性能不佳( = 0.61)。结果还表明,合适的气溶胶模型可改善VIIRS在陆地上的反演性能,特别是对于沙尘类型( 从0.61提高到0.72)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77b3/5615553/607442aaf463/ijerph-14-01016-g001.jpg

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