A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms

Author:

Tang Fucai1,Chen Peng12,An Zhiyuan3,Xiong Mingzhu1,Chen Hao1,Qiu Liangcai1

Affiliation:

1. College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China

2. State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, The Chinese Academy of Sciences, Wuhan 430077, China

3. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Abstract

Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes.

Funder

National Natural Science Foundation of China

State Key Laboratory of Geodesy and Earth’s Dynamics

Innovation Academy for Precision Measurement Science and Technology

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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