基于轨迹聚类的GNSS-IR多系统组合土壤湿度估计方法

郑南山; 何佳星; 丁锐; 章恒一

doi:10.13203/j.whugis20220743

基于轨迹聚类的GNSS-IR多系统组合土壤湿度估计方法

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

摘要

摘要: 全球导航卫星系统干涉反射测量（global navigation satellite system-interferometric reflectometry,GNSS-IR）技术能探测浅层地表的土壤湿度。针对多系统组合的土壤湿度反演问题,基于轨迹聚类方法,从全球定位系统(global positioning system,GPS)、北斗卫星导航系统（BeiDou satellite navigation system,BDS）、格洛纳斯(GLONASS)、伽利略（Galileo）导航系统的信噪比观测数据中提取多径干涉相位,利用经验模型求解轨迹聚类后的土壤湿度估计值,以加权平均方式得到系统组合后的估计结果。结果表明,BDS、Galileo反演精度相当且优于GPS、GLONASS,基于轨迹聚类的多系统组合土壤湿度估计方法的均方根误差为0.041 4 cm³/cm³,相比于单系统的综合反演精度提升约16.3%,相比于单系统的最佳频段反演精度提升约5.2%,所提方法能有效监测土壤湿度的变化。

Abstract:
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.

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