Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets
Rivers play import roles in ecological biodiversity, shipping trade, and carbon cycle. In our study, we developed an effective, robust, and accurate algorithm for national-scale river mapping, and produced the annual China river extent dataset (CRED) from 2016 to 2023. We assessed the reliability of...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11061783/ |
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| author | Kaifeng Peng Beibei Si Weiguo Jiang Meihong Ma Xuejun Wang |
| author_facet | Kaifeng Peng Beibei Si Weiguo Jiang Meihong Ma Xuejun Wang |
| author_sort | Kaifeng Peng |
| collection | DOAJ |
| description | Rivers play import roles in ecological biodiversity, shipping trade, and carbon cycle. In our study, we developed an effective, robust, and accurate algorithm for national-scale river mapping, and produced the annual China river extent dataset (CRED) from 2016 to 2023. We assessed the reliability of the CRED based on test samples and data intercomparison. The results indicated that the overall accuracies of the CRED were greater than 88.4% from 2016 to 2023. The rivers of the CRED from 2017 to 2023 achieved good accuracy, with the user accuracies, producer accuracies and F1-score of rivers exceeding 80.4%, 85.0%, and 83.7%, respectively. In 2016, rivers of the CRED achieved medium accuracy, with F1-score of 78.4%. A further data comparison indicated that our CRED had good consistency with existing river-related datasets, with correlation coefficient (R) greater than 0.75. The area statistics indicated that the river area in China were 44948.78 km<sup>2</sup> in 2023. From 2016 to 2023, the river areas were characterized by an initial increase, followed by a decrease, and then a slight increase. Spatially, the decreased rivers were located mainly in Southeast China, whereas the increased rivers were distributed mainly in Central China and Northeast China. In general, the CRED explicitly delineated river extents and dynamics in China, which could provide a good foundation for improving river ecology and management. |
| format | Article |
| id | doaj-art-03ff4154e951427fbca694abed256306 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-03ff4154e951427fbca694abed2563062025-08-20T03:28:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118166941670610.1109/JSTARS.2025.358477011061783Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived DatasetsKaifeng Peng0https://orcid.org/0000-0001-7319-9346Beibei Si1Weiguo Jiang2https://orcid.org/0000-0002-1352-1046Meihong Ma3Xuejun Wang4https://orcid.org/0000-0001-9203-3464Faculty of Geography, Tianjin Normal University, Tianjin, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaRivers play import roles in ecological biodiversity, shipping trade, and carbon cycle. In our study, we developed an effective, robust, and accurate algorithm for national-scale river mapping, and produced the annual China river extent dataset (CRED) from 2016 to 2023. We assessed the reliability of the CRED based on test samples and data intercomparison. The results indicated that the overall accuracies of the CRED were greater than 88.4% from 2016 to 2023. The rivers of the CRED from 2017 to 2023 achieved good accuracy, with the user accuracies, producer accuracies and F1-score of rivers exceeding 80.4%, 85.0%, and 83.7%, respectively. In 2016, rivers of the CRED achieved medium accuracy, with F1-score of 78.4%. A further data comparison indicated that our CRED had good consistency with existing river-related datasets, with correlation coefficient (R) greater than 0.75. The area statistics indicated that the river area in China were 44948.78 km<sup>2</sup> in 2023. From 2016 to 2023, the river areas were characterized by an initial increase, followed by a decrease, and then a slight increase. Spatially, the decreased rivers were located mainly in Southeast China, whereas the increased rivers were distributed mainly in Central China and Northeast China. In general, the CRED explicitly delineated river extents and dynamics in China, which could provide a good foundation for improving river ecology and management.https://ieeexplore.ieee.org/document/11061783/10 m spatial resolutionannual changesChinariver mappingsentinel-derived datasets |
| spellingShingle | Kaifeng Peng Beibei Si Weiguo Jiang Meihong Ma Xuejun Wang Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 m spatial resolution annual changes China river mapping sentinel-derived datasets |
| title | Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets |
| title_full | Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets |
| title_fullStr | Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets |
| title_full_unstemmed | Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets |
| title_short | Exploring the Annual Dynamics of China’s Rivers From 2016 to 2023 Based on Sentinel-Derived Datasets |
| title_sort | exploring the annual dynamics of china x2019 s rivers from 2016 to 2023 based on sentinel derived datasets |
| topic | 10 m spatial resolution annual changes China river mapping sentinel-derived datasets |
| url | https://ieeexplore.ieee.org/document/11061783/ |
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