Comparison of five sand-dust distribution quantitative identification method based on Himawari-8

Tao Han; Dawei Wang; Youyan Jiang; Enqing Shen

doi:10.1117/12.2536627

14 February 2020 Comparison of five sand-dust distribution quantitative identification method based on Himawari-8

Tao Han, Dawei Wang, Youyan Jiang, Enqing Shen

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1143206 (2020) https://doi.org/10.1117/12.2536627
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

In recent years, with the frequencies and intensities was increasing in environmental issues, much more attentions were focus on sandstorm in the fields of natural science and social science research. The geostationary satellite imagery can continuously observe the surface of the earth in a short period of time, and has a good monitoring advantage for the sand- dust, which has a fast-moving target. Based on the geostationary meteorological satellite data of Himawari-8(H8) at 4:00 on May 3, 2017, the results of remote sensing retrieval of sand-dust intensity are compared by using a variety of exist sand- dust identification models. The results show that the multi-channel threshold method has the best effect on sand-dust identification, the reflected radiation dust index method is the second, and the infrared split window channel difference method and the infrared split window channel ratio method have the worst identification effect. Single channel threshold method, infrared multi-channel threshold method, infrared split window channel difference method and infrared split window channel ratio method have poor distinction between cloud layer and sand dust, multi-channel threshold method and reflected radiation dust index method are poor distinguished between low temperature zone and the sand-dust.

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Tao Han, Dawei Wang, Youyan Jiang, and Enqing Shen "Comparison of five sand-dust distribution quantitative identification method based on Himawari-8", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143206 (14 February 2020); https://doi.org/10.1117/12.2536627

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KEYWORDS

Infrared radiation

Clouds

Satellites

Remote sensing

Reflectivity

Composites

Meteorological satellites

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