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Remote Sensing Technology and Application  2018, Vol. 33 Issue (5): 793-802    DOI: 10.11873/j.issn.1004-0323.2018.5.0793
    
Downscaling of Remotely Sensed Land Surface Temperature with the BP Neural Network
Wang Zihao1,Qin Qiming1,2,3,Sun Yuanheng1,Zhang Tianyuan1,Ren Huazhong1
(1.Institute of Remote Sensing and Geographical Information System,School of Earth and Space Sciences,Peking University,Beijing 100871,China;2.Beijing Key Lab of Spatial InformationIntegration and Its Application,Peking University,Beijing 100871,China;3.National Surveyingand Mapping Geographic Information Engineering Technology Center of GeographicInformation Basic Software and Application,Beijing 100871,China)
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Abstract  Downscaling algorithms based on statistical models have been widely utilized to address the issue of coarse-resolution Land Surface Temperature (LST).However,most methods (e.g.,TsHARP algorithm) could be affected by land environment,including land cover,seasons.In this study,a Back Propagation (BP) neural network was introduced for LST downscaling in a specific area with complex land covers.The method comprises two steps.First,five reprehensive spectral indices were selected to training according to three typical land cover,including vegetation,building,and water.And the structure of network was trained using coarse-resolution spectral indices and LST.Second,high-resolution spectral indices were input to the network to get a high-resolution LST.A stratified linear regression downscaling with land-cover classification was conducted for comparative evaluation.The comparative results showed that in urban,vegetation,and water areas,the Root Mean Square Error (RMSE),determination coefficient (R2),and relative accuracy for the proposed approach (BP neural network) were better than those for stratified linear regression.Finally,the verification results show that RMSE and bias of the algorithm are 0.98 ℃ and 0.51 ℃,which is obviously better than the result of stratified linear regression (RMSE is 2.9 ℃ and Bias is 1.7 ℃).It shows that this method has a higher downscaling accuracy.And the approach is potential for producing high-resolution LST for the study on urban thermal environment.
Key words:  Land Surface Temperature(LST)      Downscaling      BP neural network      Spectral indices      Landsat 8 OLI     
Received:  24 November 2017      Published:  29 December 2018
ZTFLH:  TP 79  
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Wang Zihao
Qin Qiming
Sun Yuanheng

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Wang Zihao, Qin Qiming, Sun Yuanheng. Downscaling of Remotely Sensed Land Surface Temperature with the BP Neural Network. Remote Sensing Technology and Application, 2018, 33(5): 793-802.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.5.0793     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I5/793

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