Academic Journal
Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU
| العنوان: | Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU |
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| المؤلفون: | Wei Liu, Zihui Lin, Yuan Hu, Aodong Tian, Xintai Yuan, Jens Wickert |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18235-18246 (2024) |
| بيانات النشر: | IEEE, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Bidirectional gated recurrent units (Bi-GRU), detrended signal-to-noise ratio (dSNR), gated recurrent unit (GRU) neural network, global navigation satellite system reflectometry (GNSS-R), snow depth, signal-to-noise ratio (SNR), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy and robustness, especially in complex terrains and extreme weather. This study proposes an innovative snow depth retrieval technique employing a time-series recurrent neural network with bidirectional gated recurrent units (Bi-GRUs). Unlike traditional methods using signal-to-noise ratio (SNR) features, our algorithm utilizes the detrended SNR as Bi-GRU input, aiming to enhance accuracy, particularly in low snow depths and complex terrains. SNR observations from GPS L1 carriers at stations P351 and AB33 were analyzed. The Bi-GRU algorithm demonstrated high consistency with true snow depths at station P351 (coefficient of determination: 0.9766), with the root-mean-square error (RMSE) and the mean absolute error (MAE) of 9.1559 and 6.4185 cm, respectively. Compared to traditional methods, the Bi-GRU model improved the RMSE by 30.9% and the MAE by 44.5%. At station AB33, where snow depth variations were significant, accuracy improvements of 65.6% (RMSE: 7.4905 cm) and 63.2% (MAE: 5.6074 cm) were observed. In addition, the Bi-GRU model exhibited greater robustness compared to long short-term memory. These findings highlight the efficacy of the Bi-GRU-based approach, suggesting its superiority and broader applicability. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10697422/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3470222 |
| URL الوصول: | https://doaj.org/article/00253e5eb2d24badb00473379c9e135a |
| رقم الانضمام: | edsdoj.00253e5eb2d24badb00473379c9e135a |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 19391404 21511535 |
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| DOI: | 10.1109/JSTARS.2024.3470222 |