Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data
In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting...
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GeoAI Data Society
2025-03-01
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| Series: | Geo Data |
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| Online Access: | http://geodata.kr/upload/pdf/GD-2024-0052.pdf |
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| author | DongHyeon Yoon Ha-Eun Yu Euiho Hwang Ki-mook Kang Gibeom Nam Jin-Gyeom Kim |
| author_facet | DongHyeon Yoon Ha-Eun Yu Euiho Hwang Ki-mook Kang Gibeom Nam Jin-Gyeom Kim |
| author_sort | DongHyeon Yoon |
| collection | DOAJ |
| description | In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies. |
| format | Article |
| id | doaj-art-724eb0fc3e7d49f4ae497922e0473046 |
| institution | DOAJ |
| issn | 2713-5004 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | GeoAI Data Society |
| record_format | Article |
| series | Geo Data |
| spelling | doaj-art-724eb0fc3e7d49f4ae497922e04730462025-08-20T03:04:26ZengGeoAI Data SocietyGeo Data2713-50042025-03-0171182610.22761/GD.2024.0052177Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD DataDongHyeon Yoon0Ha-Eun Yu1Euiho Hwang2Ki-mook Kang3Gibeom Nam4Jin-Gyeom Kim5Senior Researcher, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaResearcher, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaHead of Center, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaPrincipal Researcher, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaSenior Researcher, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaSenior Researcher, Water Resources Satellite Center, K-water Research Institute, 125 Yuseong-daero 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaIn this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.http://geodata.kr/upload/pdf/GD-2024-0052.pdfsynthetic aperture radarsentinel-1waterbodywater levelreservoir |
| spellingShingle | DongHyeon Yoon Ha-Eun Yu Euiho Hwang Ki-mook Kang Gibeom Nam Jin-Gyeom Kim Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data Geo Data synthetic aperture radar sentinel-1 waterbody water level reservoir |
| title | Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data |
| title_full | Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data |
| title_fullStr | Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data |
| title_full_unstemmed | Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data |
| title_short | Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data |
| title_sort | waterbody detection and reservoir water level prediction using bayesian mixture models with sentinel 1 grd data |
| topic | synthetic aperture radar sentinel-1 waterbody water level reservoir |
| url | http://geodata.kr/upload/pdf/GD-2024-0052.pdf |
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