Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning
The remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient fo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2424 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850071490201911296 |
|---|---|
| author | Genxin Song Youjing Jiang Xinyu Lei Shiyan Zhai |
| author_facet | Genxin Song Youjing Jiang Xinyu Lei Shiyan Zhai |
| author_sort | Genxin Song |
| collection | DOAJ |
| description | The remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient for high-accuracy studies. Using SSC data from the Longmen Hydrological Station (2019–2020) and Sentinel-2 imagery, multiple models were compared, and the random forest regression model was selected for its superior performance. A non-parametric regression model was developed based on optimal band combinations to estimate the SSC in high-sediment rivers. Results show that the model achieved a high coefficient of determination (R<sup>2</sup> = 0.94) and met accuracy requirements considering the maximum SSC, MAPE, and RMSE. The B4, B7, B8A, and B9 bands are highly sensitive to high-concentration sediment rivers. SSC exhibited significant seasonal and spatial variation, peaking above 30,000 mg/L in summer (July–September) and dropping below 1000 mg/L in winter, with a positive correlation with discharge. Spatially, the SSC was higher in the gorge section than in the main channel during the flood season and higher near the banks than in the river center during the dry season. Overall, the random forest model outperformed traditional methods in SSC prediction for sediment-laden rivers. |
| format | Article |
| id | doaj-art-a283afadf4aa49dc89664e1709816bfb |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a283afadf4aa49dc89664e1709816bfb2025-08-20T02:47:17ZengMDPI AGRemote Sensing2072-42922025-07-011714242410.3390/rs17142424Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine LearningGenxin Song0Youjing Jiang1Xinyu Lei2Shiyan Zhai3Faculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaFaculty of Geographical Science and Engineering, College of Geographical Sciences, Henan University, Zhengzhou 450046, ChinaThe remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient for high-accuracy studies. Using SSC data from the Longmen Hydrological Station (2019–2020) and Sentinel-2 imagery, multiple models were compared, and the random forest regression model was selected for its superior performance. A non-parametric regression model was developed based on optimal band combinations to estimate the SSC in high-sediment rivers. Results show that the model achieved a high coefficient of determination (R<sup>2</sup> = 0.94) and met accuracy requirements considering the maximum SSC, MAPE, and RMSE. The B4, B7, B8A, and B9 bands are highly sensitive to high-concentration sediment rivers. SSC exhibited significant seasonal and spatial variation, peaking above 30,000 mg/L in summer (July–September) and dropping below 1000 mg/L in winter, with a positive correlation with discharge. Spatially, the SSC was higher in the gorge section than in the main channel during the flood season and higher near the banks than in the river center during the dry season. Overall, the random forest model outperformed traditional methods in SSC prediction for sediment-laden rivers.https://www.mdpi.com/2072-4292/17/14/2424suspended sediment concentrationyellow river canyon outletrandom forestsentinel-2 images |
| spellingShingle | Genxin Song Youjing Jiang Xinyu Lei Shiyan Zhai Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning Remote Sensing suspended sediment concentration yellow river canyon outlet random forest sentinel-2 images |
| title | Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning |
| title_full | Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning |
| title_fullStr | Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning |
| title_full_unstemmed | Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning |
| title_short | Measurement of Suspended Sediment Concentration at the Outlet of the Yellow River Canyon: Using Sentinel-2 Images and Machine Learning |
| title_sort | measurement of suspended sediment concentration at the outlet of the yellow river canyon using sentinel 2 images and machine learning |
| topic | suspended sediment concentration yellow river canyon outlet random forest sentinel-2 images |
| url | https://www.mdpi.com/2072-4292/17/14/2424 |
| work_keys_str_mv | AT genxinsong measurementofsuspendedsedimentconcentrationattheoutletoftheyellowrivercanyonusingsentinel2imagesandmachinelearning AT youjingjiang measurementofsuspendedsedimentconcentrationattheoutletoftheyellowrivercanyonusingsentinel2imagesandmachinelearning AT xinyulei measurementofsuspendedsedimentconcentrationattheoutletoftheyellowrivercanyonusingsentinel2imagesandmachinelearning AT shiyanzhai measurementofsuspendedsedimentconcentrationattheoutletoftheyellowrivercanyonusingsentinel2imagesandmachinelearning |