A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Li, Wangping; Zhang, Wanchang; Li, Zhihong; Wang, Yu; Chen, Hao; Gao, Huiran; Zhou, Zhaoye; Hao, Junming; Li, Chuanhua; Wu, Xiaodong
• Is Part Of: European journal of remote sensing. Volume 55:Issue 1(2022)
• Page Start: 303
• Page End: 312
• Publication Date: 2022-12-31
• Subjects: Maximum entropy model -- spectral matching -- remote sensing -- Landsat 8_OLI -- surface water extraction -- normalized difference water index; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2022.2062054
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas, i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method.