收稿：2017-07-24，
录用：2018-2-21，
纸质出版：2018-09
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利用GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)技术反演积雪深度是近年来一种新兴的卫星遥感技术。目前大多数研究仅使用GPS(Global Position System)数据限制了该技术的发展，为了扩展GNSS-MR算法的应用，介绍了基于GNSS-MR算法的雪深反演模型。首先，通过多项式拟合分解GLONASS观测数据获取高精度的信噪比残差序列；然后，利用Lomb-Scargle谱分析法对其进行频谱分析可解算雪深值。选取IGS中心的YEL2站2015年11月到2016年6月共243天的GLONASS卫星L1波段反射信号的SNR数据进行实例分析，并以美国国家气象数据中心提供的加拿大Y-H (Yellowknife Henderson)气象站的实测雪深数据为真值，将反演雪深与实测雪深进行对比验证。所得实验结果如下：(1) 与GPS卫星的反演值相比，基于GLONASS-MR (GLONASS Multipath Reflectometry)技术反演积雪深度的精度同样能达到厘米级，RMSE仅3.3 cm，反演值与实测值的空间分布趋势一致且相关性较强，其相关系数
R
2
高达0.969；(2) 不同的积雪深度对信噪比的振幅频率与垂直反射距离具有直接影响；(3) 对同一卫星而言，信噪比的频谱振幅强度峰值与其对应的反演值存在线性相关；(4) 在相同条件下，采用多颗GLONASS卫星数据比单颗GLONASS卫星数据反演雪深的效果明显更优。基于反演的高时间分辨率产品，分析该地区雪深日变化的情况，实验结果表明基于陆基CORS站的GLONASS-MR技术在用于实时、连续的雪深变化监测方面具有良好的潜力和可行性。
Snow Depth (SD) measurements are important to hydrology
climatology
and agriculture. Global navigation satellite system multipath reflectometry (GNSS-MR) technology is a relatively new and powerful method for sensing SD. Thus far
many algorithms have been proposed to detect SD from the Signal-to-Noise Ratio (SNR) data of different satellites’ reflectometry signals. However
the relevant studies based on GLONASS satellites have less consideration for the development of existing GNSS-MR algorithms. This study aims to verify and analyze the suitability and reliability of applying GNSS-MR model for SD detection from GLONASS data. Theoretical analysis and formula derivation are conducted to systematically and quantitatively determine the SD detection. By analyzing the characteristics of the ground-based GNSS SNR data caused by multipath
this study introduces the detection model and the basic theory of GNSS-R technology based on SNR data to detect SD data. The SNR data of GLONASS L1 reflectometry signals can be obtained from the CORS (Continuously Oporating Reference Stations) network provided by the International GNSS Service (IGS). The residual sequence of this SNR serves as key knowledge for the SD detection in GNSS-R model. The SD is estimated through the spectral analysis using a Lomb-Scargle algorithm. To verify the above theory
the GLONASS-MR model is used to detect SD over the Yellowknife Henderson area in Canada. The observed period is from November 2015 to June 2016
243 days in total. Then
the GLONASS products are compared with in situ measurements of the National Climate Data Center. Tesults show the following: (1) The accuracy of the detected SD based on GLONASS-MR can reach centimeter level
and the RMSE is 3.3 cm. The detected results likewise reveal good spatial coincidence
with a correlation coefficient of 0.969. (2) The different snow depths can directly affect the amplitude frequency of GLONASS SNR data and the vertical reflection distance. (3) For the same satellite
a linear relation is discovered between the peak values of the amplitude frequency and the detected SD. (4) Under the same conditions
SD detected using more GLONASS satellites can have less bias than that from a GLONASS satellite. This study aims to add new SNR data to the GNSS-MR algorithm to improve SD detection when other satellites’ data cannot be obtained. Validation using both simulated SD data and in situ measurements indicates that the proposed method is effective and can be used for real-time and continuous SD monitoring. Therefore
this study provides references for the future development of GNSS-R technology in China.
Bilich A and Larson K M. 2007. Mapping the GPS multipath environment using the signal‐to‐noise ratio (SNR). Radio Science, 42(6): RS6003
Bilich A, Larson K M and Axelrad P. 2008. Modeling GPS phase multipath with SNR: case study from the Salar de Uyuni, Boliva. Journal of Geophysical Research, 113(B4): B04401
Comp C J and Axelrad P. 1998. Adaptive SNR-based carrier phase multipath mitigation technique. IEEE Transactions on Aerospace and Electronic Systems, 34(1): 264–276
戴凯阳, 张双成, 李振宇, 赵迎辉, 南阳. 2016. 利用GPS的SNR观测值进行雪深监测研究. 大地测量与地球动力学, 36(6): 525–528
Dai K Y, Zhang S C, Li Z Y, Zhao Y H and Nan Y. 2016. Snow depth detection with GPS SNR observations. Journal of Geodesy and Geodynamics, 36(6): 525–528 (
Jin S G and Komjathy A. 2010. GNSS reflectometry and remote sensing: new objectives and results. Advances in Space Research, 46(2): 111–117
Jin S G, Feng G P and Gleason S. 2011. Remote sensing using GNSS signals: current status and future directions. Advances in Space Research, 47(10): 1645–1653
Larson K M, Gutmann E D, Zavorotny V U, Braun J J, Williams M W and Nievinski F G. 2009. Can we measure snow depth with GPS receivers?. Geophysical Research Letters, 36(17): L17502
Larson K M, Small E E, Gutmann E, Bilich A, Axelrad P and Braun J. 2008. Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solutions, 12(3): 173–177
Larson K M and Small E E. 2016. Estimation of snow depth using L1 GPS signal-to-noise ratio data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10): 4802–4808
刘风玲. 2015. 基于GPS反射信号的海面高度反演技术研究. 上海: 上海海洋大学
Liu F L. 2015. Research on the Ocean Altimetry with GPS Reflection Signal. Shanghai: Shanghai Ocean University
Ozeki M and Heki K. 2012. GPS snow depth meter with geometry-free linear combinations of carrier phases. Journal of Geodesy, 86(3): 209–219
Qian X D and Jin S G. 2016. Estimation of snow depth from GLONASS SNR and phase-based multipath reflectometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10): 4817–4823
Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M and Miller H L. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report on the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
万玮, 陈秀万, 李国平, 曾开祥. 2012. GNSS-R遥感国内外研究进展. 遥感信息, 27(3): 112–119
Wan W, Chen X W, Li G P and Zeng K X. 2012. GNSS reflectometry: a review of theories and empirical applications in ocean and land surfaces. Remote Sensing Information, 27(3): 112–119 (
万玮, 陈秀万, 彭学峰, 白伟华, 夏俊明, 梁宏, 张学民, 熊攀, 杨婷. 2016. GNSS遥感研究与应用进展和展望. 遥感学报, 20(5): 858–874
Wan W, Chen X W, Peng X F, Bai W H, Xia J M, Liang H, Zhang X M, Xiong P and Yang T. 2016. Overview and outlook of GNSS remote sensing technology and applications. Journal of Remote Sensing, 20(5): 858–874 (
万玮, 李黄, 洪阳, 陈秀万, 彭学峰. 2015. GNSS-R遥感观测模式及陆面应用. 遥感学报, 19(6): 882–893
Wan W, Li H, Hong Y, Chen X W and Peng X F. 2015. Definition and application of GNSS-R observation patterns. Journal of Remote Sensing, 19(6): 882–893 (
王迎强, 严卫, 符养, 栾毅. 2009. 机载GPS反射信号土壤湿度测量技术. 遥感学报, 13(4): 678–690
Wang Y Q, Yan W, Fu Y and Luan Y. 2009. Soil moisture determination of reflected GPS signals from aircraft platform. Journal of Remote Sensing, 13(4): 678–690 (
徐斌, 杨涛, 谭保华, 陈益. 2011. 基于Lomb-Scargle算法的周期信号探测的模拟研究. 核电子学与探测技术, 3-1(6): 702–705
Xu B, Yang T, Tan B H and Chen Y. 2011. The simulate study of signal detection based on Lomb-Scargle algorithm. Nuclear Electronics and Detection Technology, 3-1(6): 702–705 (
于灵雪, 张树文, 卜坤, 杨久春, 颜凤芹, 常丽萍. 2013. 雪数据集研究综述. 地理科学, 33(7): 878–883
Yu L X, Zhang S W, Bu K, Yang J C, Yan F Q and Chang L P. 2013. A review on snow data sets. Scientia Geographica Sinica, 33(7): 878–883 (
Yu K, Ban W, Zhang X and Yu X. 2015. Snow depth estimation based on multipath phase combination of GPS triple-frequency signals. IEEE Transactions on Geoscience & Remote Sensing, 53(9): 5100–5109
袁林果, 黄丁发, 丁晓利, 熊永良, 钟萍, 李成钢. 2004. GPS载波相位测量中的信号多路径效应影响研究. 测绘学报, 33(3): 210–215
Yuan L G, Huang D F, Ding X L, Xiong J L, Zhong P and Li C G. 2004. On the influence signal multipath effects in GPS carrier phase surveying. Acta Geodaetica et Cartographica Sinica, 33(3): 210–215 (
赵润, 赵治华. 2009. GPS多路径误差处理技术. 测绘科学, 34(5): 21–22
Zhao R and Zhao Z H. 2009. Solution of the multipath error of GPS. Science of Surveying and Mapping, 34(5): 21–22 (
周晓中, 邵连军. 2012. GNSS-R航空遥感海面风场仿真技术. 遥感学报, 16(1): 143–153
Zhou X Z and Shao L J. 2012. Simulation techniques of GNSS-R sea surface wind field retrieval from airborne remote sensing. Journal of Remote Sensing, 16(1): 143–153 (
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