Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors
Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statisti...
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doaj-399e9508c5cf4bfaa7e2d52dbfbc86e52020-11-24T23:51:05ZengMDPI AGWater2073-44412017-04-019425610.3390/w9040256w9040256Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and SensorsYan Zhou0Jinwei Dong1Xiangming Xiao2Tong Xiao3Zhiqi Yang4Guosong Zhao5Zhenhua Zou6Yuanwei Qin7School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaDepartment of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USASatellite Environment Center, Ministry of Environmental Protection, Beijing 100094, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaDepartment of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USADepartment of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USAOpen surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.http://www.mdpi.com/2073-4441/9/4/256open surface water body mappingwater indicesSentinel-2Landsat 8Landsat 7comparison |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yan Zhou Jinwei Dong Xiangming Xiao Tong Xiao Zhiqi Yang Guosong Zhao Zhenhua Zou Yuanwei Qin |
spellingShingle |
Yan Zhou Jinwei Dong Xiangming Xiao Tong Xiao Zhiqi Yang Guosong Zhao Zhenhua Zou Yuanwei Qin Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors Water open surface water body mapping water indices Sentinel-2 Landsat 8 Landsat 7 comparison |
author_facet |
Yan Zhou Jinwei Dong Xiangming Xiao Tong Xiao Zhiqi Yang Guosong Zhao Zhenhua Zou Yuanwei Qin |
author_sort |
Yan Zhou |
title |
Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors |
title_short |
Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors |
title_full |
Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors |
title_fullStr |
Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors |
title_full_unstemmed |
Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors |
title_sort |
open surface water mapping algorithms: a comparison of water-related spectral indices and sensors |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2017-04-01 |
description |
Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts. |
topic |
open surface water body mapping water indices Sentinel-2 Landsat 8 Landsat 7 comparison |
url |
http://www.mdpi.com/2073-4441/9/4/256 |
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