An Adaptive Blended Algorithm Approach for Deriving Bathymetry from Multispectral Imagery

The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this article, we first...

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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 14; s. 801 - 817
Hlavní autoři:	Liu, Yongming, Tang, Danling, Deng, Ruru, Cao, Bin, Chen, Qidong, Zhang, Ruihao, Qin, Yan, Zhang, Shaoquan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive algorithms Adaptive optics Algorithms Bathymeters Bathymetry coral reef Data Imagery Landsat Landsat satellites Oligotrophic waters Optical imaging Optical reflection Optical sensors Optimization Reflectance Reflectivity Regression analysis Remote sensing Root-mean-square errors Satellite imagery Sensitivity analysis Shallow water Water depth Wavelength
ISSN:	1939-1404, 2151-1535
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Abstract	The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this article, we first applied a method for global sensitivity analysis to a semianalytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The subalgorithms and depth range used for each subalgorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root-mean- square error (RMSE) = 0.31 to 0.94 m for WorldView-2 data, RMSE= 0.25 to 1.42 m for Landsat-8 data). When the in situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.
AbstractList	The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this article, we first applied a method for global sensitivity analysis to a semianalytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The subalgorithms and depth range used for each subalgorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root-mean- square error (RMSE) = 0.31 to 0.94 m for WorldView-2 data, RMSE= 0.25 to 1.42 m for Landsat-8 data). When the in situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.
Author	Deng, Ruru Cao, Bin Qin, Yan Liu, Yongming Zhang, Ruihao Tang, Danling Chen, Qidong Zhang, Shaoquan
Author_xml	– sequence: 1 givenname: Yongming surname: Liu fullname: Liu, Yongming email: liuyongminga401@outlook.com organization: Guangdong Key Laboratory of Ocean Remote Sensing, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China – sequence: 2 givenname: Danling orcidid: 0000-0002-4645-8688 surname: Tang fullname: Tang, Danling email: lingzistdl@126.com organization: Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China – sequence: 3 givenname: Ruru surname: Deng fullname: Deng, Ruru email: eesdrr@mail.sysu.edu.cn organization: School of Geography and Planning, Sun Yat-sen University, Guangzhou, China – sequence: 4 givenname: Bin surname: Cao fullname: Cao, Bin email: caobinalonzo@sina.com organization: School of Geography and Planning, Sun Yat-sen University, Guangzhou, China – sequence: 5 givenname: Qidong surname: Chen fullname: Chen, Qidong email: 99810961@qq.com organization: South China Sea Institute of Planning and Environmental Research, State Oceanic Administration, Guangzhou, China – sequence: 6 givenname: Ruihao surname: Zhang fullname: Zhang, Ruihao email: 893101762@qq.com organization: School of Geography and Planning, Sun Yat-sen University, Guangzhou, China – sequence: 7 givenname: Yan surname: Qin fullname: Qin, Yan email: qin.yan@foxmail.com organization: Guangdong Research Institute of Water Resources and Hydropower, Guangzhou, China – sequence: 8 givenname: Shaoquan surname: Zhang fullname: Zhang, Shaoquan organization: Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, China
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SubjectTerms	Adaptive algorithms Adaptive optics Algorithms Bathymeters Bathymetry coral reef Data Imagery Landsat Landsat satellites Oligotrophic waters Optical imaging Optical reflection Optical sensors Optimization Reflectance Reflectivity Regression analysis Remote sensing Root-mean-square errors Satellite imagery Sensitivity analysis Shallow water Water depth Wavelength
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Title	An Adaptive Blended Algorithm Approach for Deriving Bathymetry from Multispectral Imagery
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