顾及地形影响的GNSS-MR雪深反演研究

Research on GNSS-MR snow depth inversion incorporating terrain factors

  • 摘要: 积雪的累积与消融受到全球天气与气候系统的影响,同时也对其演变过程产生反馈作用. 因此,开展高精度的积雪监测具有重要意义. 全球导航卫星系统多路径反射测量(GNSS multipath reflectometry,GNSS-MR)作为一种新兴的雪深探测方法,具备连续、无源等优势,然而其测量精度易受测站周边复杂地形条件干扰,整体精度相较于传统方法存在一定差距. 为了削弱地形起伏对反演结果精度的影响,本文对测站周围区域进行了平面格网划分,并对各格网单元加以约束,剔除地形起伏显著区域所产生的反射信号. 在此基础上,以美国板块边界观测(Plate Boundary Observatory,PBO)网络中的P360测站为例,计算了2015年年积日第50~100天之间的雪深变化,并与未使用该方法计算得到的结果进行对比. 实验结果显示,引入约束后的反演结果的均方根误差(root mean square error,RMSE)由12.04 cm显著降低至2.06 cm,误差降低幅度达82.89%,验证了该方法在减弱地形干扰、提升雪深反演精度方面的有效性.

     

    Abstract: Snow cover monitoring is important because the accumulation and melting of snow cover is influenced by global weather and climate, as well as changes in weather and climate. GNSS multipath reflectometry (GNSS-MR) is an emerging method for snow depth detection, but its accuracy is lower than that of other methods due to the severe influence of the undulating terrain around the station. In order to weaken the influence of terrain undulation on the accuracy of the inversion results, this paper conducts a plane grid of the terrain around the station, and constrains the results of each grid to eliminate the reflected signal in the area with excessive undulation. In this paper, taking the P360 station of the Plate Boundary Observatory (PBO) network as an example, the snow depth value from the 50th year to the 100th year of 2015 is calculated and compared with the results obtained without this method. The experimental results show that the root mean square (RMSE) calculated by this method is increased by 82.89% from 12.04 cm to 2.06 cm compared with the RMSE without this method, which proves that the method can better avoid the influence of terrain and improve the inversion accuracy.

     

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