基于多元线性回归的GPS-IR积雪深度反演研究

利用全球导航卫星系统反射干涉遥感技术（GPS-Interferometric Reflectometry， GPS-IR）可实现地表环境参数的监测。基于全球导航卫星系统多径反射信号与积雪深度之间的关系，针对目前已有研究较少的考虑多星融合对反演效果的影响，提出一种基于多元线性回归的多星融合积雪深度反演模型。为了验证算法的可靠性，利用美国PBO观测网络中的P101测站连续监测数据进行雪深反演研究。研究和实验表明：反演结果与积雪深度参考值具有显著相关性；多星融合能够有效综合各单颗卫星的反演性能，相关系数均大于0.940，相比单星提高了13.6%；RMSE和MAE均小于0.08和0.165。

Research on GPS-IR Snow Depth Inversion based on Multiple Linear Regression

The use of GPS-Interferometric Reflectometry （GPS-IR） can realize the monitoring of surface environmental parameters. Based on the relationship between multi-path reflected signals of GNSS and snow depth， this paper proposes a multi-star fusion product based on multiple linear regression based on the consideration of the influence of multi-star fusion on the inversion effect. Snow depth inversion model. In order to verify the reliability of the algorithm， the snow depth inversion research was performed using the continuous monitoring data from the P101 station in the PBO observation network in the United States. Theoretical and experimental results show that the inversion results have a significant correlation with the snow depth reference value； multi-star fusion can effectively synthesize the inversion performance of each single satellite， and the correlation coefficients are all greater than 0.940， which is at least 13.6% higher than the single star； Both RMSE and MAE are less than 0.08 and 0.165.