The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

Author:

Chen Jie1,Wang Yankun2,Wang Jingzhe13ORCID,Zhang Yinghui4ORCID,Xu Yue5,Yang Ou13,Zhang Rui6,Wang Jing7,Wang Zhensheng8,Lu Feidong9,Hu Zhongwen4ORCID

Affiliation:

1. Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic University, Shenzhen 518055, China

2. Internet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, China

3. School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China

4. MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

5. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

6. School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China

7. Vanke School of Public Health, Tsinghua University, Beijing 100091, China

8. Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory, Shenzhen 518000, China

9. Tongji Architectural Design (Group) Co., Ltd., Shanghai 200092, China

Abstract

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Funder

Guangdong Basic and Applied Basic Research Foundation

Basic Research Program of Shenzhen

Specific Innovation Program of the Department of Education of Guangdong Province

Shenzhen Polytechnic Research Fund

Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area

Guangdong Key Scientific Research Platform and Projects for the Higher-educational Institution

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3