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Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels
Abstract
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<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<0.05) and 2.17 m/year (dry, P<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.
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Metadata
- Authors
- Li, M., Zhao, C., Duan, Z., Cheng, H., Lian, Y., & Wang, G.
- Affiliations
- Beijing Normal University (CN)
- Centre National de la Recherche Scientifique (FR)
- Hohai University (CN)
- Nanjing Hydraulic Research Institute (CN)
- Corresponding author
- Zhao, Changsen
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 643
- Pages
- 131913
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W4402163571
- Cited by
- 9
- References
- 88
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 1
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Hydrology (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
- Physical Sciences › Earth and Planetary Sciences › Geophysics › Synthetic Aperture Radar (SAR) Applications and Techniques
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Keywords
- Sentinel-1, Sentinel-2, backscatter intensity, EVI, time series analysis, MERIT Hydro, vegetation area, China rivers
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