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Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation
GPS Solutions ( IF 4.9 ) Pub Date : 2020-03-04 , DOI: 10.1007/s10291-020-0966-4
Zhiyu Zhang , Fei Guo , Xiaohong Zhang

Snow is an important water resource and plays a critical role in the hydrologic cycle. Accurate measurements of snow depth are needed by scientists to set up a more refined meteorology–hydrology model. Recently, the Global Navigation Satellite System Reflectometry (GNSS-R) has been developed and applied for snow depth monitoring, with low cost and high resolution. We propose an improved snow depth retrieval method using a combination of GNSS triple-frequency carrier phase. The topographic feature of the reflecting surface is considered for estimating the snow depth by using the density-based spatial clustering of applications with noise algorithm and normalization method. Observables from the GNSS station in Alaska, USA, are used to monitor snow depth and compared with the ground-truth measurements. Compared with the traditional triple-frequency snow depth retrieval method, the new approach has better performance for Galileo and BDS. The RMSE of the snow depth estimate reduces by nearly 40%, and the correlation coefficient increases from 0.93 to 0.97 for Galileo and from 0.91 to 0.95 for BDS, respectively. The research findings show no notable deviations on snow depth average estimation between Galileo and BDS observations compared to the GPS ones. Moreover, the solution with the proposed method results in improving spatial resolution due to the increasing number of satellites and better azimuth coverage.

中文翻译:

利用聚类和归一化算法补偿地形变化的三频多GNSS反射法雪深反演

雪是重要的水资源,在水文循环中起着关键作用。科学家需要准确测量雪深,以建立更完善的气象水文学模型。最近,全球导航卫星系统反射法(GNSS-R)已开发并以低成本和高分辨率应用于雪深监测。我们提出了一种结合GNSS三频载波相位的改进的雪深检索方法。通过使用基于密度的应用程序空间聚类以及噪声算法和归一化方法,可以考虑反射面的地形特征来估计雪深。来自美国阿拉斯加GNSS站的观测数据用于监测积雪深度,并与地面真相测量结果进行比较。与传统的三频雪深检索方法相比,新方法对伽利略和BDS具有更好的性能。积雪深度估计值的RMSE降低了近40%,Galileo的相关系数从0.93增加到0.97,BDS的相关系数从0.91增加到0.95。研究结果表明,与GPS观测相比,伽利略和BDS观测之间的雪深平均估算值没有显着偏差。而且,由于卫星数目的增加和更好的方位角覆盖,所提出的方法的解决方案导致了空间分辨率的提高。BDS分别为95。研究结果表明,与GPS观测相比,伽利略和BDS观测之间的雪深平均估算值没有显着偏差。而且,由于卫星数目的增加和更好的方位角覆盖,所提出的方法的解决方案导致了空间分辨率的提高。BDS分别为95。研究结果表明,与GPS观测相比,伽利略和BDS观测之间的雪深平均估算值没有显着偏差。而且,由于卫星数目的增加和更好的方位角覆盖,所提出的方法的解决方案导致了空间分辨率的提高。
更新日期:2020-03-04
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