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Remote Sensing Technology and Application  2019, Vol. 34 Issue (3): 602-611    DOI: 10.11873/j.issn.1004-0323.2019.3.0602〖
    
An Albedo Downscaling Method based on Stratified Linear Regression
Feng Chanying1,Wang Zihao,Zheng Chengyang2
(1.College of Urban and Environmental Sciences,Peking University,Beijing 100871;
2.Institute of Remote Sensing and Geographical Information System,School of Earth and Space Sciences,Peking University,Beijing 100871)
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Abstract  In recent years,remote sensing technology has been widely used in the field of surface energy,thermal environment,and climate research in small and medium-sized regions.The demand for high resolution,high precision albedo products has increased.In view of this,a physical-based downscaling method is proposed for efficiently and accurately generating high-resolution albedo results.First,under the Lambert hypothesis,the primary albedo of Landsat 8 could be obtained based on Landsat 8 reflectance at 30m resolution.On the 500 m scale,it is found that after classification,the primary albedo of Landsat 8 has a better correlation with the broad-band albedo of MODIS MCD43A3 product.Therefore,a linear regression function based on surface classification is established to integrate the high-resolution primary albedo of Landsat 8 with high-precision MCD43A3 albedo to obtain downscaled albedo.Compared to MODIS albedo,the downscaled albedo provided more rich details.Verification experience based on SURFRAD observation data shows that Bias of the albedo downscaling is 0.01,and standard deviation is 0.012,which has good adaptability for different surface categories.It shows that the algorithm has great application value for producing high-resolution albedo products.
Key words:  Albedo      Downscale      Stratified linear regression      Landsat 8      MODIS     
Received:  08 May 2018      Published:  09 July 2019
ZTFLH:  TP75  
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Cite this article: 

Feng Chanying, Wang Zihao, Zheng Chengyang. An Albedo Downscaling Method based on Stratified Linear Regression. Remote Sensing Technology and Application, 2019, 34(3): 602-611.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.3.0602〖     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I3/602

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