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Remote Sensing Technology and Application  2018, Vol. 33 Issue (2): 241-251    DOI: 10.11873/j.issn.1004-0323.2018.2.0241
    
Retrieve Snow Depth of North of Xinjiang Region from ARMS 2 Data based on Artificial Neural Network Technology
Hou Haiyan1,2,Hou Jinliang1,Huang Chunlin1,Wang Yunchen1,2
(1.Laboratory of Remote Sensing and Geospatial Science,Northwest Institute of Eco\|Environmentand Resources,Chinese Academy of Sciences,Lanzhou 730000,China;2.University of Chinese Academy of Sciences,Beijing 100049,China)
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Abstract  Based on the characteristics of the microwave signal responding to the snow depth,we use AMSR2 brightness temperature,geo\|location and terrain factor as the inputs of ANN,and snow depth as the desired output to develop an efficiency snow depth retrieve model.We compared the influence of combinations of TB,geo-ocation and terrain factors on the retrieve of snow depth.It is reviewed in this article that,TB of horizontal polarization,latitude perform better than vertical polarization and longitude respectively.Combination of slope and aspect is superior to other combinations of terrain factors.Besides,there are equivalent influence on snow depth of geo\|location and terrain factors.Finally,we compare the performance of four optimal ANN models under different input combinations.At last,we found that the ANN consists TB,latitude,longitude,slope and aspect as inputs is the best model which might fairly simulating the snow depth of Beijiang.
Key words:  Brightness temperature;Snow depth;ANN;Geo\      location;Terrain     
Received:  19 November 2017      Published:  15 May 2018
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Hou Haiyan
Hou Jinliang
Huang Chunlin
Wang Yunchen

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Hou Haiyan,Hou Jinliang,Huang Chunlin,Wang Yunchen. Retrieve Snow Depth of North of Xinjiang Region from ARMS 2 Data based on Artificial Neural Network Technology. Remote Sensing Technology and Application, 2018, 33(2): 241-251.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.2.0241     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I2/241

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