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Remote Sensing Technology and Application  2018, Vol. 33 Issue (5): 881-889    DOI: 10.11873/j.issn.1004-0323.2018.5.0881
    
Simulation of Solar Radiation based on BP Neural Network andIts Spatio-temporal Change Analysis in East China
 Feng Jiaojiao1,2,Wang Weizhen1,Li Jing3,Liuwenwen4
(1.Key Laboratory of Remote Sensing of Gansu Province,Heihe Remote Sensing Experimental 
Research Station,Northwest Institute of Eco\|Environment and Resources,Chinese Academy of 
Sciences,Lanzhou,730000,China;2.University of Chinese Academy of Sciences,Beijing,100049,China;
3.The College of Geographical and Environmental Science,Northwest Normal University,
Lanzhou,730070,China;4.Surveying and Mapping Product Quality Supervision and Inspection Stationin Gansu Province,Lanzhou,730000,China)
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Abstract  Solar radiation data are important parameters of crop model,hydrological model and climate change model,however,the distribution of solar radiation sites is scarce and uneven throughout the country,and it is difficult to obtain spatial continuous solar radiation by using only rare radia.Therefore,the lack of solar radiation data restricts the development of the relevant model,and the neural network on the solar radiation has a good predictability,many Artificial Neural Network ensemble models were developed to estimate solar radiation using routinely measured meteorolological variables,but it did not consider cloud,aerosol,and precipitable water vapor influence on solar radiation.In this paper,we used cloud,aerosols,atmospheric precipitable water vapor from MODIS atmosphere remote sensing products and conventional meteorological data including air pressure,temperature,sunshine duration and latitude and elevation,based on the LM\|BP neural network model to simulate the 90 conventional weather stations in Eastern China from 2001 to 2014.The results show that the model has a good fit of 0.95,and the root mean square error is controlled within 2 MJ·m-2.The average deviation error is between -1 MJ·m-2 and 1 MJ·m-2.Finally,using the simulated values of the model and the measured values of 13 radiation sites,the spatial distribution of the annual solar radiation in the East China region from 2001 to 2014 is obtained by spatial interpolation and the spatial variation trend is analyzed. 
Key words:         Solar radiation;Neural network;MODIS;Cloud;Aerosol;Water vapor;Spatio-temporal change     
Received:  18 November 2017     
P422.1  
  TP79  
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Feng Jiaojiao
Wang Weizhen
Li Jing
Liuwenwen

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Feng Jiaojiao,Wang Weizhen,Li Jing,Liuwenwen. Simulation of Solar Radiation based on BP Neural Network andIts Spatio-temporal Change Analysis in East China. Remote Sensing Technology and Application, 2018, 33(5): 881-889.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.5.0881     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I5/881

[1] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 1 -6 .
[2] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 7 -13 .
[3] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 14 -17 .
[4] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 18 -22 .
[5] . TM Remote Sensing and Yield Variability of Wheat within Fields[J]. Remote Sensing Technology and Application, 2001, 16(1): 23 -27 .
[6] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 28 -31 .
[7] . A Study on Monitoring Frost of Main Crop in the Area ofNingxia by Using Remote Sensing[J]. Remote Sensing Technology and Application, 2001, 16(1): 32 -36 .
[8] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 37 -39 .
[9] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 40 -44 .
[10] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 45 -48 .