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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 40-47     DOI: 10.6046/gtzyyg.2020215
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Impervious surface extraction based on Sentinel-2A and Landsat8
ZHAO Yi1,2,3,4(), XU Jianhui1,2, ZHONG Kaiwen1(), WANG Yunpeng3,4, HU Hongda1,2, WU Pinghao1,2,3,4
1. Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Laboratory for Geographic Spatio-temporal Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
2. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
3. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
4. University of the Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

The extraction of impervious surface (IS) is very important for the development of cities, and linear spectral mixture analysis is commonly adopted to calculate the fraction of IS in the mixed pixel to improve the extraction of the urban IS at the subpixel scale. Owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this paper, the modified endmember selection was proposed to improve the accuracy of the spectral information of endmembers. Sentinel-2A images were applied to selected endmembers to get the spectral, which was used to modify the spectral information of the endmembers from Landsat8. In addition, the optimization scheme of LSMA results in which the normalized differential vegetation index (NDVI) and dry bare-soil index (DBSI) thresholds are used to optimize the mixing results was applied to improve the accuracy of LSMA results. With the WorldView-2 remote sensing image for sample verification, the results showed that the accuracy of IS fraction extracted by the method in this paper was 20% higher than that of the traditional method, providing reliable theoretical support for endmember selection and IS extraction.
Keywords Sentinel-2A      modified endmember selection      linear spectral mixture analysis      impervious surface      Sentinel-2A      modified endmember selection      linear spectral mixture analysis      impervious surface     
ZTFLH:  TP79X87  
Corresponding Authors: ZHONG Kaiwen     E-mail: zhaoyiww@gdas.ac.cn;zkw@gdas.ac.cn
Issue Date: 21 July 2021
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Jianhui XU
Kaiwen ZHONG
Yi ZHAO
Jianhui XU
Yunpeng WANG
Kaiwen ZHONG
Hongda HU
Yunpeng WANG
Pinghao WU
Hongda HU
Pinghao WU
Cite this article:   
Jianhui XU,Kaiwen ZHONG,Yi ZHAO, et al. Impervious surface extraction based on Sentinel-2A and Landsat8[J]. Remote Sensing for Land & Resources, 2021, 33(2): 40-47.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020215     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/40
Fig.1  Study area image combined with Landsat8 B4(R),B3(G),B2(B)
Landsat8 OLI Sentinel-2A MSI
波段 空间分
辨率/m 		波长/μm 波段 空间分
辨率/m 		波长/μm
B1 30 0.433~0.453
B2 30 0.450~0.515 B2 10 0.458~0.523
B3 30 0.525~0.600 B3 10 0.543~0.578
B4 30 0.630~0.680 B4 10 0.650~0.680
B5 30 0.845~0.885 B8 10 0.785~0.900
B6 30 1.560~1.660 B11 20 0.855~0.875
B7 30 2.100~2.300 B12 20 2.100~2.280
Tab.1  Landsat8 OLI and Sentinel-2A MSI data
Landsat8 OLI Sentinel-2A MSI
波段 空间分
辨率/m 		波长/μm 波段 空间分
辨率/m 		波长/μm
B1 30 0.433~0.453
B2 30 0.450~0.515 B2 10 0.458~0.523
B3 30 0.525~0.600 B3 10 0.543~0.578
B4 30 0.630~0.680 B4 10 0.650~0.680
B5 30 0.845~0.885 B8 10 0.785~0.900
B6 30 1.560~1.660 B11 20 0.855~0.875
B7 30 2.100~2.300 B12 20 2.100~2.280
Tab.1  Landsat8 OLI and Sentinel-2A MSI data
Fig.2  Flow chart
Fig.2  Flow chart
Fig.3  Spectral curves of endmembers
Fig.3  Spectral curves of endmembers
Fig.4  Distribution of impervious surface fraction
Fig.4  Distribution of impervious surface fraction
Fig.5  Linear fitting scatter diagram
Fig.5  Linear fitting scatter diagram
研究方法 SE MAE RMSE R2
传统LSMA 0.195 0.240 0.298 0.735
结合端元优化方案的LSMA 0.190 0.217 0.265 0.851
结合端元优化和解混结果优化方案的LSMA -0.066 0.103 0.133 0.879
Tab.2  accuracy assessment of urban IS fraction extracted by different methods
研究方法 SE MAE RMSE R2
传统LSMA 0.195 0.240 0.298 0.735
结合端元优化方案的LSMA 0.190 0.217 0.265 0.851
结合端元优化和解混结果优化方案的LSMA -0.066 0.103 0.133 0.879
Tab.2  accuracy assessment of urban IS fraction extracted by different methods
Fig.6  Details for impervious surface
Fig.6  Details for impervious surface
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