A neural network method for monitoring snowstorm: A case study in southern China

文献类型： 外文期刊

第一作者： Mao Kebiao

作者： Mao Kebiao;Xia Lang;Mao Kebiao;Ma Ying;Xia Lang;Mao Kebiao;Ma Ying;He Tianjue;Shen Xinyi;Sun Zhiwen;Zhou Guanhua

作者机构：

关键词： snowstorm;neural network;snow depth;passive microwave;Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)

期刊名称：CHINESE GEOGRAPHICAL SCIENCE （ 影响因子：2.892； 五年影响因子：2.721 ）

ISSN： 1002-0063

年卷期： 2014 年 24 卷 5 期

页码：

收录情况： SCI

摘要： It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learning neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7V and TB36.5V, TB18.7H and TB36.5H, TB23.8V and TB89V, TB23.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

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