基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法

土壤湿度的监测是全球卫星导航系统干涉测量法（GNSS-IR）的关键应用之一。传统的GNSS-IR土壤湿度反演方法一般只针对单颗卫星的单一频段，未充分利用不同轨道、不同频率卫星信号的差异性与互补性。针对此问题，提出了一种将GPS多星的L1、L2和L5频段数据加权融合进行联合反演的方法，该方法利用基于最小方差的自适应融合算法得到加权因子，并通过现场实验进行了方法验证。结果表明:在测试集上所提出的反演方法相比于传统的Larson方法，相关系数提高了24.69%，均方根误差下降了22.28%，与均值融合法相比，相关系数提高了26.77%，均方根误差下降了23.26%，证明了所提方法能有效提高反演精度。 

A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion

Soil moisture monitoring is one of the key applications of Global Navigation Satellite System Interferometry and Reflectometry (GNSS-IR). Traditional GNSS-IR soil moisture inversion methods generally utilize only one frequency of single satellite, which lose the opportunities of taking full advantages of difference and complementarity of satellite signals with different orbits and frequencies. To solve this problem, this paper proposes a joint inversion method with weighting fusions of the L1, L2 and L5 frequency band data of GPS multi-satellite. In this method, the weighting factor is determined by an adaptive fusion algorithm based on the minimum variance. Field experiment is performed for verification. The results show that, compared with traditional Larson method on the test set, the correlation coefficient and the root-mean-square error of the inversion method proposed in this paper are 24.69% higher and 22.28% lower respectively, and meanwhile compared with the fusion method of the mean value method, the correlation coefficient and the root-mean-square error are 26.77% higher and 23.26% lower respectively. Experimental results prove that the proposed method can effectively improve the inversion accuracy.