Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels

•The mechanical method accurately estimates subpixel river width in vegetated areas.•We make linear σ-EVI functions by successfully removing vegetation growth noise.•Its accuracy is higher than globally widely-used river-width datasets.•The method breaks the internationally accepted 3-time-resolutio...

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2024-11, Vol.643, p.131913, Article 131913
Hauptverfasser: Li, Maomao, Zhao, Changsen, Duan, Zhen, Cheng, Hongguang, Lian, Yanqing, Wang, Guoqing
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container_title Journal of hydrology (Amsterdam)
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creator Li, Maomao
Zhao, Changsen
Duan, Zhen
Cheng, Hongguang
Lian, Yanqing
Wang, Guoqing
description •The mechanical method accurately estimates subpixel river width in vegetated areas.•We make linear σ-EVI functions by successfully removing vegetation growth noise.•Its accuracy is higher than globally widely-used river-width datasets.•The method breaks the internationally accepted 3-time-resolution-accuracy limitation. Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P
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Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P&lt;0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P&lt;0.05) and 2.17 m/year (dry, P&lt;0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). 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Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P&lt;0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P&lt;0.05) and 2.17 m/year (dry, P&lt;0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.</description><subject>Backscatter intensity</subject><subject>China</subject><subject>data collection</subject><subject>glaciers</subject><subject>hydrology</subject><subject>mechanical methods</subject><subject>rain</subject><subject>rivers</subject><subject>satellites</subject><subject>Subpixel unmixing</subject><subject>summer</subject><subject>time series analysis</subject><subject>vegetation</subject><subject>Vegetation growth</subject><subject>vegetation index</subject><subject>Water surface width</subject><subject>watersheds</subject><subject>Yellow River</subject><issn>0022-1694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkMlOwzAURbMAiTJ8ApKXbFI8ZfAKoYpJKmIBrC3XfmkcuUmxnZb-Ax-NS7vHsjy9e4_8bpZdEzwlmJS33bRrd8YPbkox5VPCiCDsJJtgTGlOSsHPsvMQOpwGY3yS_byCblVvtXJuh1RvkNJ69CpCuqZHPbp0Rt5uwKOtNbFFtkcbWEJU0Q49Uh5UQIskHsa1s_0SvUMfbQ8uJ3-8nCK7UkvwFkIixDb5XSrk2wT2-cp-g0HrtLpwmZ02ygW4Ou4X2efjw8fsOZ-_Pb3M7ue5phTHvICaVBWriGkENWliUZeUKyKwUYtGF1hQzMEYUVeYN2TBFa8rIJwAr1hZsIvs5sBd--FrhBDlygYNLn0LhjFIRgpWcyEYTdLiINV-CMFDI9c-teN3kmC5T1x28pi43CcuD4kn393Bl9qCjQUvg7bQazDWg47SDPYfwi-Vl5As</recordid><startdate>202411</startdate><enddate>202411</enddate><creator>Li, Maomao</creator><creator>Zhao, Changsen</creator><creator>Duan, Zhen</creator><creator>Cheng, Hongguang</creator><creator>Lian, Yanqing</creator><creator>Wang, Guoqing</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-2655-236X</orcidid></search><sort><creationdate>202411</creationdate><title>Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels</title><author>Li, Maomao ; Zhao, Changsen ; Duan, Zhen ; Cheng, Hongguang ; Lian, Yanqing ; Wang, Guoqing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c220t-5e8177371df92d92d098624a190dabfc509204edd98704f1b4a487e141e473653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Backscatter intensity</topic><topic>China</topic><topic>data collection</topic><topic>glaciers</topic><topic>hydrology</topic><topic>mechanical methods</topic><topic>rain</topic><topic>rivers</topic><topic>satellites</topic><topic>Subpixel unmixing</topic><topic>summer</topic><topic>time series analysis</topic><topic>vegetation</topic><topic>Vegetation growth</topic><topic>vegetation index</topic><topic>Water surface width</topic><topic>watersheds</topic><topic>Yellow River</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Maomao</creatorcontrib><creatorcontrib>Zhao, Changsen</creatorcontrib><creatorcontrib>Duan, Zhen</creatorcontrib><creatorcontrib>Cheng, Hongguang</creatorcontrib><creatorcontrib>Lian, Yanqing</creatorcontrib><creatorcontrib>Wang, Guoqing</creatorcontrib><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of hydrology (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Maomao</au><au>Zhao, Changsen</au><au>Duan, Zhen</au><au>Cheng, Hongguang</au><au>Lian, Yanqing</au><au>Wang, Guoqing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels</atitle><jtitle>Journal of hydrology (Amsterdam)</jtitle><date>2024-11</date><risdate>2024</risdate><volume>643</volume><spage>131913</spage><pages>131913-</pages><artnum>131913</artnum><issn>0022-1694</issn><abstract>•The mechanical method accurately estimates subpixel river width in vegetated areas.•We make linear σ-EVI functions by successfully removing vegetation growth noise.•Its accuracy is higher than globally widely-used river-width datasets.•The method breaks the internationally accepted 3-time-resolution-accuracy limitation. Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P&lt;0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P&lt;0.05) and 2.17 m/year (dry, P&lt;0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2024.131913</doi><orcidid>https://orcid.org/0000-0002-2655-236X</orcidid></addata></record>
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subjects Backscatter intensity
China
data collection
glaciers
hydrology
mechanical methods
rain
rivers
satellites
Subpixel unmixing
summer
time series analysis
vegetation
Vegetation growth
vegetation index
Water surface width
watersheds
Yellow River
title Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels
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