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Global Lake Ice Thickness Estimation Based on Multi-GNSS Reflected Signals from Tianmu-1 Constellation
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Global Lake Ice Thickness Estimation Based on Multi-GNSS Reflected Signals from Tianmu-1 Constellation
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Journal Article
Global Lake Ice Thickness Estimation Based on Multi-GNSS Reflected Signals from Tianmu-1 Constellation
2025
Overview
As an important component of the cryosphere, lake ice plays an important role in regulating regional climate and maintaining the balance of lake ecosystems. Lake ice thickness is one of the core parameters to study the dynamic processes of lake ice. However, there are relatively few studies on lake ice thickness estimation based on spaceborne GNSS reflected signals. Therefore, this paper presents a method for global lake ice thickness estimation using Multi-GNSS reflected signals from the Tianmu-1 constellation. By combining multi-system GNSS-R data (BDS-R/GPS-R/GLONASS-R/Galileo-R) with the ERA5 dataset, we adopt a hybrid deep learning framework combining convolutional neural network (CNN) and Bi-directional long short-term memory network (BiLSTM) for retrieving lake ice thickness. The experimental results show that the method has high accuracy, with the best performance of the BDS system for retrieving lake ice thickness (RMSE: 0.137m, CC: 0.95). Index Terms —Global Navigation Satellite System-Reflectometry (GNSS-R); Tianmu-1; Lake ice thickness.
Publisher
IOP Publishing
