A GNSS-IR Multi-system Combination Soil Moisture Estimation Method Based on Track Clustering

被引：0
|
作者：
Zheng N. [1 ,2 ]
He J. [1 ,2 ]
Ding R. [1 ,2 ]
Zhang H. [1 ]
机构：
[1] School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou
[2] Key Laboratory of Land Environment and Disaster Monitoring, MNR, Xuzhou
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2024年 / 49卷 / 01期
关键词：
global navigation satellite system-interferometric reflectometry (GNSS-IR); multi-system combination; signal-to-noise ratio (SNR); soil moisture; track clustering;
D O I：
10.13203/j.whugis20220743
中图分类号：
学科分类号：
摘要：
Objectives: Global navigation satellite system-interferometric reflectometry can be taken advantage of identifying soil moisture of land surface. Methods: Aiming at the problem of multi-GNSS combined soil moisture inversion, we use the phase extracted from the signal-to-noise ratio observation data of global positioning system (GPS), BeiDou satellite navigation system (BDS), GLONASS and Galileo system, and solve soil moisture inversion within consideration of satellite track clustering by using the empirical model. The estimation of multi-GNSS combination is obtained by weighted average method. Results: The inversion accuracy of BDS and Galileo is equivalent and superior to GPS and GLONASS. The root mean square error of the method in this paper is 0.041 4 cm3/cm3, which is about 16.3% and 5.2% lower than that of single navigation satellite system and the optimal frequency band respectively. Conclusions: The results indicate that the multi-GNSS reflection signal estimation method based on track clustering can effectively monitor changes in soil moisture. © 2024 Editorial Department of Geomatics and Information Science of Wuhan University. All rights reserved.
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页码：37 / 46
页数：9
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