Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data

WEI Xin; REN Yu; CHEN Xidong; HU Qingfeng; LIU Hui; ZHOU Jing; SONG Dongwei; ZHANG Peipei; HUANG Zhiquan

doi:10.6046/zrzyyg.2022445

2024 Vol. 36, No. 2

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WEI Xin, REN Yu, CHEN Xidong, HU Qingfeng, LIU Hui, ZHOU Jing, SONG Dongwei, ZHANG Peipei, HUANG Zhiquan. 2024. Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data. Remote Sensing for Natural Resources, 36(2): 268-278. doi: 10.6046/zrzyyg.2022445

Citation:

WEI Xin, REN Yu, CHEN Xidong, HU Qingfeng, LIU Hui, ZHOU Jing, SONG Dongwei, ZHANG Peipei, HUANG Zhiquan. 2024. Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data. Remote Sensing for Natural Resources, 36(2): 268-278. doi: 10.6046/zrzyyg.2022445

Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data

1. College of Surveying, Mapping and Geographic Information, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
;2. College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China
;3. Seventh Geological Brigade, Henan Nonferrous Metals Geological and Mineral Bureau, Zhengzhou 450045, China
;4. Henan Surveying and Mapping Institute, Zhengzhou 450045, China
;5. Luoyang Institute of Science and Technology, Luoyang 471023, China

More Information

Corresponding author: CHEN Xidong

Received Date: 17 November 2022

Revised Date: 02 March 2023

Publish Date: 14 June 2024

Abstract

Abstract

Inland water bodies, as irreplaceable resources in ecosystems, play a vital role in climate change and regional water circulation. Scientifically and accurately monitoring the distribution and dynamic changes of water bodies is critical for ecosystem balance maintenance, sustainable human development, and early warning of floods and droughts. However, current research primarily focuses on the static monitoring of inland water bodies, lacking high-resolution monitoring of dynamic changes in water bodies. Hence, relying on the Google Earth Engine (GEE) cloud computing platform, this study monitored the dynamic changes of water bodies at a spatial resolution of 10 m, with the Sentinel-2 surface reflectance data in 2020 as the data source. First, the optimal water body monitoring features were selected by examining the features of typical land cover types in Sentinel-2 spectral bands and water indices. Then, an automatic extraction method for water body training datasets was proposed in conjunction with priori water body products, obtaining high-confidence water body training samples. Furthermore, the spectral angle (SA) and Euclidean distance (ED) methods were integrated based on the Dempster-Shafer (D-S) evidence theory model, and a SA-ED dynamic monitoring model for water bodies was developed combined with the extracted optimal water body monitoring features. Finally, the stability of the SA-ED model was tested with Henan Province as a study area, demonstrating that the SA-ED model can effectively monitor the dynamic changes in water bodies. The SA-ED model yielded an overall monitoring accuracy of 97.03% for water bodies in Henan Province, with user accuracy of 95.85% and producer accuracy of 95.17% for permanent water bodies, user and producer accuracies of 96.21% and 93.82% for seasonal water bodies, respectively. The results of this study provide a novel approach for the fine-resolution monitoring of dynamic changes in water bodies.
- inland water body /
- water body distribution /
- dynamic monitoring /
- Google Earth Engine /
- Sentinel-2