Snow depth estimation based on GNSS-IR cluster analysis

作     者：Zhang, Shuangcheng Zhang, Chenglong Zhao, Ying Li, Hao Liu, Qi Pang, Xiaoguang 

作者机构：Changan Univ Coll Geol Engn & Geomant Xian Shaanxi Peoples R China China Meteorol Adm Urumqi Inst Desert Meteorol Urumqi Xinjiang Peoples R China 

出 版 物：《MEASUREMENT SCIENCE AND TECHNOLOGY》 (测量科学与技术)

年 卷 期：2021年第32卷第9期

页      面：095801-095801页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：State Key Laboratory of Geo-Information Engineering [SKLGIE2019-Z-2-1] National Key R&D Program of China [2020YFC1512000, 2019YFC1509802, 2018YFC1505102] Natural Science Foundation of China projects (NSFC) [42074041, 41731066] Shaanxi Natural Science Research Program [2020JM-227] Fundamental Research Funds for the Central Universities [300102269201, 300102299206] 

主　　题：ground-based GNSS-IR Altay signal-to-noise ratio dynamic clustering algorithm Grubbs criterion 

摘      要：As the most influential factor of the cryosphere, snow is the weathervane of climate change. Real-time and accurate snow monitoring data play an important role in climate-change indication, water resource management, disaster prevention, and mitigation. As traditional snow monitoring cannot meet the current requirements, since 2007, the signal-to-noise ratio data has been used for snow-depth inversion. The accuracy of the long time-series global navigation satellite system interferometric reflectometry (GNSS-IR) is not high, although it is significantly better compared to the previous methods. When the ground is covered by shallow snow or there is no snow, the snow-depth inversion is affected by the vegetation and snow layer, lowering the reliability of the Lomb-Scargle spectrum (LSP) analysis, and reducing the snow-depth inversion accuracy. To address the instability of the LSP results, in this study, the dynamic clustering algorithm is used for screening the PSD of the LSP results, and the influence of the signal penetration is eliminated. The average peak of the frequency based on multi-satellite LSP is obtained, and finally, the Grubbs criterion is utilized for improving the reliability of the results. The data of the Altay GNSS snow monitoring station at Altay in Xinjiang and the plate boundary observation SG27 and P351 sites are used as the research data, and a representative time period for the snow depth is selected. The inversion snow depth of the traditional GNSS-IR method are compared with those of the improved GNSS-IR. The experimental results demonstrate that improved GNSS-IR results had better match with the measured snow depth.