High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data
Lake shrinkage and water scarcity are frequent problems in arid and semiarid regions; monitoring the variations of surface water using remote sensing images is useful for disaster prevention and water resource management. Here, we proposed models using Sentinel-2 images based on the light gradient b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223001000 |
| _version_ | 1797842781459185664 |
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| author | Bohao Li Kai Liu Ming Wang Yanfang Wang Qian He Linmei Zhuang Weihua Zhu |
| author_facet | Bohao Li Kai Liu Ming Wang Yanfang Wang Qian He Linmei Zhuang Weihua Zhu |
| author_sort | Bohao Li |
| collection | DOAJ |
| description | Lake shrinkage and water scarcity are frequent problems in arid and semiarid regions; monitoring the variations of surface water using remote sensing images is useful for disaster prevention and water resource management. Here, we proposed models using Sentinel-2 images based on the light gradient boosting machine (LightGBM) to quantify the monthly surface water dynamics in the middle farming-pastoral ecotone of the Northern China (M-FPENC) region, which was facing severe water security challenges from 2016 to 2021, at a 10-m resolution. The results show that the proposed models perform very well, with average values over 99.9% derived for four classification metrics; LightGBM is time-efficient and robust in different scenarios for surface water dynamic monitoring. The maps produced by these models could capture the details of surface water accurately in the M-FPENC region; the surface water area of the M-FPENC region showed a clear seasonal pattern, showing the largest average extent in June (856.9 km2) and the smallest average extent in November (486.6 km2); the annual average maximum water area was 1030.9 km2, of which 630.9 km2 was seasonal and 400 km2 was permanent. The proposed product can provide decision support for local water resources planning. |
| first_indexed | 2024-04-09T16:54:53Z |
| format | Article |
| id | doaj.art-88a4873e60b7414aa922e64b63a4b96d |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T16:54:53Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-88a4873e60b7414aa922e64b63a4b96d2023-04-21T06:41:38ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-04-01118103278High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI dataBohao Li0Kai Liu1Ming Wang2Yanfang Wang3Qian He4Linmei Zhuang5Weihua Zhu6School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave, Beijing 100875, ChinaSchool of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave, Beijing 100875, China; Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China; Corresponding author at: School of National Safety and Emergency Management, Beijing Normal University, #19 Xinjiekou Wai Ave., Beijing 100875, China.School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave, Beijing 100875, ChinaHebei International Joint Research Centre for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, ChinaSchool of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave, Beijing 100875, ChinaSchool of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave, Beijing 100875, ChinaTransport Planning and Research Institute, Ministry of Transport, Beijing 100028, ChinaLake shrinkage and water scarcity are frequent problems in arid and semiarid regions; monitoring the variations of surface water using remote sensing images is useful for disaster prevention and water resource management. Here, we proposed models using Sentinel-2 images based on the light gradient boosting machine (LightGBM) to quantify the monthly surface water dynamics in the middle farming-pastoral ecotone of the Northern China (M-FPENC) region, which was facing severe water security challenges from 2016 to 2021, at a 10-m resolution. The results show that the proposed models perform very well, with average values over 99.9% derived for four classification metrics; LightGBM is time-efficient and robust in different scenarios for surface water dynamic monitoring. The maps produced by these models could capture the details of surface water accurately in the M-FPENC region; the surface water area of the M-FPENC region showed a clear seasonal pattern, showing the largest average extent in June (856.9 km2) and the smallest average extent in November (486.6 km2); the annual average maximum water area was 1030.9 km2, of which 630.9 km2 was seasonal and 400 km2 was permanent. The proposed product can provide decision support for local water resources planning.http://www.sciencedirect.com/science/article/pii/S1569843223001000Sentinel-2Surface water dynamicsFarming-pastoral ecotone of Northern China (FPENC)Machine learningLightGBM |
| spellingShingle | Bohao Li Kai Liu Ming Wang Yanfang Wang Qian He Linmei Zhuang Weihua Zhu High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data International Journal of Applied Earth Observations and Geoinformation Sentinel-2 Surface water dynamics Farming-pastoral ecotone of Northern China (FPENC) Machine learning LightGBM |
| title | High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data |
| title_full | High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data |
| title_fullStr | High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data |
| title_full_unstemmed | High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data |
| title_short | High-spatiotemporal-resolution dynamic water monitoring using LightGBM model and Sentinel-2 MSI data |
| title_sort | high spatiotemporal resolution dynamic water monitoring using lightgbm model and sentinel 2 msi data |
| topic | Sentinel-2 Surface water dynamics Farming-pastoral ecotone of Northern China (FPENC) Machine learning LightGBM |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223001000 |
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