Abstract In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network . The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth, Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.

中文翻译：

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GNSS-IR雪深反演算法精度分析

摘要 近年来，随着全球导航卫星系统（GNSS）的不断发展，它不仅应用于导航定位，还应用于地表环境监测。目前在进行GNSS-IR（GNSS Interferometric Reflectometry）雪深反演时，主要采用Lomb-Scargle Periodogram（LSP）频谱分析来计算天线相位中心到反射面的垂直高度。但是，它存在功率谱分析识别度低的问题，可能导致频率泄漏。因此，本文引入了快速傅里叶变换（FFT）频谱分析和非线性最小二乘拟合（NLSF）来计算垂直高度。GNSS-IR雪深反演实验是利用美国PBO（Plate Boundary Observatory）网络P351站2013-2016年的观测数据进行的，采用三种算法进行雪深反演并与实际进行对比雪深由 SNOTEL 网络中的 490 站提供。分别利用L1和L2波段的观测数据寻找不同算法之间的最优组合，进一步提高GNSS-IR雪深反演的精度。对于 L1 波段，不同的雪深对应不同的优化算法。当雪深小于0.8m时，NLSF算法的反演精度最高。当雪深大于0.8m时，FFT算法反演精度更高。因此，根据积雪深度的不同，针对GNSS-IR雪深反演提出了NLSF+FFT组合算法。与传统的LSP算法相比，组合算法的反演精度提高了10%。对于L2波段数据，结果表明，各种算法的雪深反演精度不随雪深的变化而变化，在三种单一算法中，FFT算法的反演精度优于LSP和NLSF算法。