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Monitoring River–Lake Dynamics in the Mid-Lower Reaches of the Yangtze River Using Sentinel-2 Imagery and X-Means Clustering.

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Title: Monitoring River–Lake Dynamics in the Mid-Lower Reaches of the Yangtze River Using Sentinel-2 Imagery and X-Means Clustering.
Authors: Qi, Zhanshuo1,2 (AUTHOR), Yao, Shiming1,2 (AUTHOR) yaosm@mail.crsri.cn, Liu, Xiaoguang2,3 (AUTHOR), Ding, Bing2,3,4 (AUTHOR), Wang, Hongyang1,2,3 (AUTHOR), Jiang, Yuqi1,2 (AUTHOR), Hu, Jinpeng3,4 (AUTHOR)
Source: Remote Sensing. Oct2025, Vol. 17 Issue 20, p3421. 23p.
Subjects: Rapids, Water management, Drought management, Hydrologic models, Remote-sensing images, Bodies of water, Clustering algorithms, Water supply
Geographic Terms: Yangtze River (China), Poyang Lake (China)
Abstract: Highlights: What are the main findings? A seasonal surface water area (SWA) product for the mid-lower Yangtze River (MLRYR) was developed using a multidimensional X-means clustering algorithm with Sentinel-2 imagery. Over the past six years, MLRYR's SWA remained stable overall, but significant declines were observed in Poyang Lake, Dongting Lake, and Shijiu Lake, while Danjiangkou Reservoir showed the largest area increase. What is the implication of the main finding? The study provides a robust framework for monitoring surface water dynamics, applicable to other regions, enhancing water resource management and conservation strategies. The findings reveal the complex interplay of climatic factors with hydrological buffering by river networks, informing targeted drought impact mitigation. River–lake systems are essential for sustaining ecosystems and human livelihoods. However, the complexity and variability of large river–lake systems, coupled with characteristic differences in water bodies across regions, have made quantifying their extent and changes inherently challenging. This study implements a robust water extraction method based on the multidimensional X-means clustering algorithm. This method leverages the advantages of Sentinel-2 imagery for water detection. Utilizing the X-means algorithm, it generates a new seasonal surface water area (SWA) product for the mid-lower reaches of the Yangtze River (MLRYR). The implemented method achieved an overall accuracy of 97.98%, a producer's accuracy of 98.02%, a user's accuracy of 96.01%, a Matthews correlation coefficient of 0.954, and a Kappa coefficient of 0.954. Analysis of water body dynamics reveals that over the past six years, the overall trend of SWA in the MLRYR has remained stable. However, within a broad range including multiple sub-basins, a decline in SWA has been observed on an inter-annual scale. Among the large lakes and reservoirs in the MLRYR, the water areas of Poyang Lake, Dongting Lake and Shijiu Lake all showed a marked decline. Among all water bodies with a significant increase in area, the Danjiangkou Reservoir is the largest. Further correlation analysis indicates that SWA exhibited the strongest correlations with precipitation and drought index in most sub-basins. In sub-basins where large lakes and reservoirs exist, the presence of river networks played a buffering role by regulating and storing water, thereby reducing the direct influence of climatic factors on lake and reservoir water extent. These findings highlight the complex interplay of climatic and hydrological factors. By integrating satellite imagery and Earth observation, this study advances understanding of MLRYR surface water dynamics, providing a robust framework for monitoring in other regions. It offers critical insights into drought impacts and informs effective water resource management and conservation strategies. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Monitoring River–Lake Dynamics in the Mid-Lower Reaches of the Yangtze River Using Sentinel-2 Imagery and X-Means Clustering.
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  Data: <searchLink fieldCode="AR" term="%22Qi%2C+Zhanshuo%22">Qi, Zhanshuo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Shiming%22">Yao, Shiming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yaosm@mail.crsri.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaoguang%22">Liu, Xiaoguang</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Bing%22">Ding, Bing</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hongyang%22">Wang, Hongyang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yuqi%22">Jiang, Yuqi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Jinpeng%22">Hu, Jinpeng</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Oct2025, Vol. 17 Issue 20, p3421. 23p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Rapids%22">Rapids</searchLink><br /><searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br /><searchLink fieldCode="DE" term="%22Drought+management%22">Drought management</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Bodies+of+water%22">Bodies of water</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Water+supply%22">Water supply</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Yangtze+River+%28China%29%22">Yangtze River (China)</searchLink><br /><searchLink fieldCode="DE" term="%22Poyang+Lake+%28China%29%22">Poyang Lake (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A seasonal surface water area (SWA) product for the mid-lower Yangtze River (MLRYR) was developed using a multidimensional X-means clustering algorithm with Sentinel-2 imagery. Over the past six years, MLRYR's SWA remained stable overall, but significant declines were observed in Poyang Lake, Dongting Lake, and Shijiu Lake, while Danjiangkou Reservoir showed the largest area increase. What is the implication of the main finding? The study provides a robust framework for monitoring surface water dynamics, applicable to other regions, enhancing water resource management and conservation strategies. The findings reveal the complex interplay of climatic factors with hydrological buffering by river networks, informing targeted drought impact mitigation. River–lake systems are essential for sustaining ecosystems and human livelihoods. However, the complexity and variability of large river–lake systems, coupled with characteristic differences in water bodies across regions, have made quantifying their extent and changes inherently challenging. This study implements a robust water extraction method based on the multidimensional X-means clustering algorithm. This method leverages the advantages of Sentinel-2 imagery for water detection. Utilizing the X-means algorithm, it generates a new seasonal surface water area (SWA) product for the mid-lower reaches of the Yangtze River (MLRYR). The implemented method achieved an overall accuracy of 97.98%, a producer's accuracy of 98.02%, a user's accuracy of 96.01%, a Matthews correlation coefficient of 0.954, and a Kappa coefficient of 0.954. Analysis of water body dynamics reveals that over the past six years, the overall trend of SWA in the MLRYR has remained stable. However, within a broad range including multiple sub-basins, a decline in SWA has been observed on an inter-annual scale. Among the large lakes and reservoirs in the MLRYR, the water areas of Poyang Lake, Dongting Lake and Shijiu Lake all showed a marked decline. Among all water bodies with a significant increase in area, the Danjiangkou Reservoir is the largest. Further correlation analysis indicates that SWA exhibited the strongest correlations with precipitation and drought index in most sub-basins. In sub-basins where large lakes and reservoirs exist, the presence of river networks played a buffering role by regulating and storing water, thereby reducing the direct influence of climatic factors on lake and reservoir water extent. These findings highlight the complex interplay of climatic and hydrological factors. By integrating satellite imagery and Earth observation, this study advances understanding of MLRYR surface water dynamics, providing a robust framework for monitoring in other regions. It offers critical insights into drought impacts and informs effective water resource management and conservation strategies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs17203421
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 3421
    Subjects:
      – SubjectFull: Rapids
        Type: general
      – SubjectFull: Water management
        Type: general
      – SubjectFull: Drought management
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
      – SubjectFull: Remote-sensing images
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      – SubjectFull: Bodies of water
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Water supply
        Type: general
      – SubjectFull: Yangtze River (China)
        Type: general
      – SubjectFull: Poyang Lake (China)
        Type: general
    Titles:
      – TitleFull: Monitoring River–Lake Dynamics in the Mid-Lower Reaches of the Yangtze River Using Sentinel-2 Imagery and X-Means Clustering.
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            NameFull: Qi, Zhanshuo
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              Text: Oct2025
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              Y: 2025
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