River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data
River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are...
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doaj-6dab883702e248c4be00081d612e44332021-07-23T14:04:02ZengMDPI AGRemote Sensing2072-42922021-07-01132650265010.3390/rs13142650River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM DataDan Li0Ge Wang1Chao Qin2Baosheng Wu3State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, ChinaRiver discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R<sup>2</sup>, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R<sup>2</sup> value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R<sup>2</sup> values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.https://www.mdpi.com/2072-4292/13/14/2650Sentinel-2 imagerybankfull dischargedownstream hydraulic geometrymachine learningGoogle Earth Engineriver width |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dan Li Ge Wang Chao Qin Baosheng Wu |
spellingShingle |
Dan Li Ge Wang Chao Qin Baosheng Wu River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data Remote Sensing Sentinel-2 imagery bankfull discharge downstream hydraulic geometry machine learning Google Earth Engine river width |
author_facet |
Dan Li Ge Wang Chao Qin Baosheng Wu |
author_sort |
Dan Li |
title |
River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data |
title_short |
River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data |
title_full |
River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data |
title_fullStr |
River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data |
title_full_unstemmed |
River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data |
title_sort |
river extraction under bankfull discharge conditions based on sentinel-2 imagery and dem data |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-07-01 |
description |
River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R<sup>2</sup>, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R<sup>2</sup> value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R<sup>2</sup> values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology. |
topic |
Sentinel-2 imagery bankfull discharge downstream hydraulic geometry machine learning Google Earth Engine river width |
url |
https://www.mdpi.com/2072-4292/13/14/2650 |
work_keys_str_mv |
AT danli riverextractionunderbankfulldischargeconditionsbasedonsentinel2imageryanddemdata AT gewang riverextractionunderbankfulldischargeconditionsbasedonsentinel2imageryanddemdata AT chaoqin riverextractionunderbankfulldischargeconditionsbasedonsentinel2imageryanddemdata AT baoshengwu riverextractionunderbankfulldischargeconditionsbasedonsentinel2imageryanddemdata |
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1721286100549369856 |