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REMOTE SENSING FOR LAND & RESOURCES    2002, Vol. 14 Issue (4) : 14-16     DOI: 10.6046/gtzyyg.2002.04.03
Technology Application |
THE APPLICATION OF GMS SATELLITE TO THE REMOTE SENSING MONITORING OF RAINSTORM DISASTER
LIU Wen1, GONG Dian-li1, ZHAO Yu-jin2, ZHANG Shan-jun2
1. Shandong Meteorological Institute, Jinan 250031, China;
2. Shandong Meteorological Center, Jinan 250031, China
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Abstract  

Using GMS-5 infrared, visible and vapor multi-channel remote sensing data of high space-time resolution as well as general meteorological data, this paper has formulated the satellite remote sensing image models for monitoring summer rainstorm of Shandong. The methods for using meteorological satellite data to detect rainstorm disaster are studied.

Keywords        Spectral index space      Independent component analysis(ICA)      Zn contamination stress      Hyperspectral remote sensing      Remote sensing recognition model       
Issue Date: 02 August 2011
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LIN Ting
LIU Xiang-Nan
TAN Zheng
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LIN Ting,LIU Xiang-Nan,TAN Zheng. THE APPLICATION OF GMS SATELLITE TO THE REMOTE SENSING MONITORING OF RAINSTORM DISASTER[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(4): 14-16.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.04.03     OR     https://www.gtzyyg.com/EN/Y2002/V14/I4/14


[1] 刘文,等.GMS多通道资料综合分析可降水云[J].气象,1998,24(2):24-26.


[2] 中国气象局科教司.省地气象台站短期预报员岗位培训教材[M].北京:气象出版社,1998.

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