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Remote Sensing Technology and Application  2020, Vol. 35 Issue (1): 141-152    DOI: 10.11873/j.issn.1004-0323.2020.1.0141
    
Atmospheric Correction of GF-1/WFV Image in Taihu Lake based on the 6S Model Pixel by Pixel
Chunmei Cheng1(),Yuchun Wei2(),Yuan Li3,Qianguang Tu1
1. College of Geomatics and Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
2. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
3. School of Tourism and Urban-rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China
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Abstract  

The spectral information of water is weak, and the commonly used radiation transfer model has poor accuracy in atmospheric correction of water body. Based on the Gaofen-1 WFV image (GF-1/WFV) and the synchronous in situ spectra covering Taihu Lake on 29th, April, 2016, the sensitivity analysis of the input parameters in 6S model was first performed, and then the image was corrected using 6S model using the observation geometry calculated pixel-by-pixel, the partitioned aerosol type and the Aerosol Optical Depth (AOD) determined by the partitioned dark pixel and Spline interpolation. The experimental results show that the aerosol type has the greatest influence on the 6S atmospheric correction results. Compared with the FLAASH method, the 6S method using the observation geometry and aerosol parameters calculated pixel-by-pixel significantly improved the atmospheric correction accuracy, with the ARE (Average Relative Error) of the four bands reduced by 1.84%,7.78%,4.79%,17%. The 6S atmospheric correction method pixel by pixel with the input of accurate atmospheric parameters can improve the correction accuracy of the remote sensing reflectance above water surface.

Key words:  GF-1      WFV      Atmospheric correction      6S      Taihu Lake     
Received:  07 October 2018      Published:  01 April 2020
ZTFLH:  TP79  
Corresponding Authors:  Yuchun Wei     E-mail:  ccm8711@163.com;weiyuchun@njnu.edu.cn
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Chunmei Cheng
Yuchun Wei
Yuan Li
Qianguang Tu

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Chunmei Cheng,Yuchun Wei,Yuan Li,Qianguang Tu. Atmospheric Correction of GF-1/WFV Image in Taihu Lake based on the 6S Model Pixel by Pixel. Remote Sensing Technology and Application, 2020, 35(1): 141-152.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.1.0141     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I1/141

Fig.1  Sampling distribution of Taihu Lake
Fig.2  Thein situ remote sensing reflectance and the simulated WFV band reflectance
Fig.3  The remote sensing reflectance corrected by different aerosol types at three points
Fig.4  The corrected remote sensing reflectance of three points using different AOD
原始影像 裁剪后影像
ID 太阳天顶角/° 太阳方位角/° 卫星天顶角/° 太阳天顶角/° 太阳方位角/° 卫星天顶角/°
1 19.33 152.61 17.89 18.71 155.70 9.10
2 18.50 159.87 0.26 18.45 158.06 6.46
3 17.30 149.58 17.89 18.11 154.67 8.95
4 16.37 157.36 0.26 17.87 157.39 8.14
Table 1  The observed geometry at the four corners in the original and clipped image
Fig.5  The AOD distribution of Taihu Lake based on dark pixels in partitioned areas and Spline interpolation
Fig.6  Atmospheric correction flow chart by the 6S pixel-by-pixel method
Fig.7  Remote sensing images (true color) and remote sensing reflectance before and after atmospheric correction
Fig.8  Comparison of 6S and FLAASH atmospheric correction results with measured spectra
6S逐像元 FLAASH模型
ARE/% RMSE ARE/% RMSE
波段1 13.65 0.007 2 15.49 0.008 5
波段2 13.8 0.008 5 21.58 0.011 0
波段3 16.35 0.006 8 21.14 0.008 4
波段4 108.3 0.014 9 125.3 0.017 0
平均值 38.02 0.009 4 45.87 0.011 2
Table 2  The AREs and RMSEs of the 6S and FLAASH atmospheric correction
Fig.9  AREs of atmospheric correction by 6S pixel by pixel model and FLAASH
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