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Water 2018, 10(5), 653;

Subpixel Surface Water Extraction (SSWE) Using Landsat 8 OLI Data

Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangzhou 510070, China
Author to whom correspondence should be addressed.
Received: 22 March 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
(This article belongs to the Section Water Resources Management and Governance)
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Surface water extraction from remote sensing imagery has been a very active research topic in recent years, as this problem is essential for monitoring the environment, ecosystems, climate, and so on. In order to extract surface water accurately, we developed a new subpixel surface water extraction (SSWE) method, which includes three steps. Firstly, a new all bands water index (ABWI) was developed for pure water pixel extraction. Secondly, the mixed water–land pixels were extracted by a morphological dilation operation. Thirdly, the water fractions within the mixed water–land pixels were estimated by local multiple endmember spectral mixture analysis (MESMA). The proposed ABWI and SSWE have been evaluated by using three data sets collected by the Landsat 8 Operational Land Imager (OLI). Results show that the accuracy of ABWI is higher than that of the normalized difference water index (NDWI). According to the obtained surface water maps, the proposed SSWE shows better performance than the automated subpixel water mapping method (ASWM). Specifically, the root-mean-square error (RMSE) obtained by our SSWE for the data sets considered in experiments is 0.117, which is better than that obtained by ASWM (0.143). In conclusion, the SSWE can be used to extract surface water with high accuracy, especially in areas with optically complex aquatic environments. View Full-Text
Keywords: subpixel surface water extraction; all bands water index; Landsat 8 OLI; mixed water–land pixels; multiple endmember spectral mixture analysis subpixel surface water extraction; all bands water index; Landsat 8 OLI; mixed water–land pixels; multiple endmember spectral mixture analysis

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Xiong, L.; Deng, R.; Li, J.; Liu, X.; Qin, Y.; Liang, Y.; Liu, Y. Subpixel Surface Water Extraction (SSWE) Using Landsat 8 OLI Data. Water 2018, 10, 653.

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