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Article

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
College of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China
3
Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area, Hunan Natural Resources Affairs Center, Changsha 410004, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(19), 4655; https://doi.org/10.3390/rs15194655
Submission received: 21 August 2023 / Revised: 17 September 2023 / Accepted: 21 September 2023 / Published: 22 September 2023
(This article belongs to the Topic Hydrology and Water Resources Management)

Abstract

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.
Keywords: Sentinel-1; generalized Stokes decomposition; dual-polarization data; water area–water level elevation relationship; Dongting Lake Sentinel-1; generalized Stokes decomposition; dual-polarization data; water area–water level elevation relationship; Dongting Lake
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MDPI and ACS Style

Song, Q.; Zhao, R.; Fu, H.; Zhu, J.; Li, Y. Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition. Remote Sens. 2023, 15, 4655. https://doi.org/10.3390/rs15194655

AMA Style

Song Q, Zhao R, Fu H, Zhu J, Li Y. Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition. Remote Sensing. 2023; 15(19):4655. https://doi.org/10.3390/rs15194655

Chicago/Turabian Style

Song, Qing, Rong Zhao, Haiqiang Fu, Jianjun Zhu, and Yi Li. 2023. "Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition" Remote Sensing 15, no. 19: 4655. https://doi.org/10.3390/rs15194655

APA Style

Song, Q., Zhao, R., Fu, H., Zhu, J., & Li, Y. (2023). Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition. Remote Sensing, 15(19), 4655. https://doi.org/10.3390/rs15194655

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