GNSS-IR解译地表环境参数研究进展及展望

周昕; 张双成; 张勤; 刘奇; 马中民; 刘宁

doi:10.12265/j.gnss.2023061

GNSS-IR解译地表环境参数研究进展及展望

doi: 10.12265/j.gnss.2023061

周昕^1,,
张双成^{1, 2, ,},
张勤^1,,
刘奇^1,,
马中民^1,,
刘宁^1,

1.
长安大学地质工程与测绘学院, 西安 710054
2.
地理信息工程国家重点实验室, 西安 710054

基金项目: 国家自然科学基金(42074041，42127802)；国家重点研发计划(2019YFC1509802)；地理信息工程国家重点实验室基金(SKLGIE2022-ZZ2-07)

详细信息

作者简介:
周昕：(1998—)，男，博士研究生，主要研究方向为地基GNSS遥感与应用

张双成：(1979—)，男，博士生导师，副教授，主要研究方向为卫星导航与定位、GNSS遥感、地质灾害监测预警等研究

张勤：(1958—)，女，博士生导师，教授，主要研究方向为空间大地测量GPS与InSAR高精度数据处理与应用、GNSS遥感、地质灾害监测与防治等

刘奇：(1994—)，男，博士研究生，主要研究方向为GNSS遥感理论与应用

通信作者:
张双成 E-mail: shuangcheng369@chd.edu.cn

中图分类号: P227
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-26

Research progress and prospects of GNSS-IR interpretation of surface environmental parameters

ZHOU Xin^1
,,
ZHANG Shuangcheng^{1, 2
, ,},
ZHANG Qin^1
,,
LIU Qi^1
,,
MA Zhongmin^1
,,
LIU Ning^1
,

1.
School of Geological Engineering and Geomatics Chang’an University, 710054, China
2.
State Key Laboratory of Geo-Information Engineering, 710054, China

摘要

摘要: 全球卫星导航系统(GNSS)具有全天候、近实时、高精度的特点，可持续发射L波段信号，广泛应用于定位、导航和授时(PNT). 随着GNSS研究与应用的不断深入，全球定位系统干涉反射(GNSS-IR)技术为地表参数探测提供了一种全新的手段. GNSS无线电导航信号经不同地表介质(如土壤、积雪、水面等)反射后，被反射的GNSS多路径信号承载反射面的特性信息，通过对GNSS反射信号中振幅、相位和频率等参数的分析，可有效获取地表反射面的物理参数. GNSS-IR作为当前GNSS和遥感领域的研究热点，取得了一些研究进展和成果. 本文详细介绍了GNSS-IR原理和方法及该技术在土壤湿度、植被、积雪和水位等方面的应用进展，并在此基础上，提出GNSS-IR研究中存在的问题及发展方向.
- 全球卫星导航系统(GNSS) /
- 多路径效应 /
- 信噪比(SNR) /
- 地表环境参数
Abstract: The Global Navigation Satellite System (GNSS) has the characteristics of all-weather, near real-time, and high accuracy, and can continuously transmit L-band signals, which are widely used for positioning, navigation, and timing (PNT). As the research and application of GNSS continue to grow, the Global Positioning System Interferometric Reflectometry (GNSS-IR) technology provides a new means of surface parameter detection. After the GNSS radio navigation signal is reflected by different surface media (such as soil, snow, water surface, etc.), the reflected GNSS multipath signals carry the characteristic information of the reflecting surface, and the physical parameters of the surface reflecting surface can be effectively obtained through the analysis of parameters such as amplitude, phase, and frequency in GNSS reflecting signals. GNSS-IR, as a current research hotspot in the field of GNSS and remote sensing, has made some research progresses and achievements. This paper introduces in detail the principle and method of GNSS-IR and the progress of the application of this technology in soil moisture, vegetation, snow and water level. Based on this, problems and development directions in GNSS-IR research are presented.
- Global Navigation Satellite System(GNSS) /
- multi-path effect /
- signal-to-noise ratio /
- surface environmental parameters

HTML全文

图 1 测站多路径误差与高度角对比图

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图 2 PRN06卫星信号的SNR变化图

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图 3 GNSS-IR技术示意图

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图 4 PRN07卫星SNR及去趋势项后的SNR观测值

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图 5 GNSS解译土壤湿度与位移监测序列^[30]

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图 6 形变速率与解译土壤湿度关系^[30]

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图 7 相位与原位土壤湿度比较^[45]

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图 8 负振幅和MODIS NDVI对比结果^[45]

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图 9 软件主界面^[62]

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图 10 SC02和GTGU站点估算的海面高度变化^[73]

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参考文献(94)

[1]	万玮, 李黄, 洪阳, 等. GNSS-R遥感观测模式及陆面应用[J]. 遥感学报, 2015, 19(6): 882-893.
[2]	彭学峰, 万玮, 李飞, 等. GNSS-R土壤水分遥感的适宜性分析[J]. 遥感学报, 2017, 21(3): 341-350.
[3]	AXELRAD P, COMP C J, MACDORAN P F. SNR-based multipath error correction for GPS differential phase[J]. IEEE transactions on aerospace and electronic systems, 1996, 32(2): 650-660. DOI: 10.1109/7.489508
[4]	BILICH A, LARSON K M, AXELRAD P. Observations of signal-to-noise ratios (SNR) at geodetic GPS site CASA: implications for phase multipath[J]. Proceedings of the centre for European geodynamics and seismology, 2004, 23: 77-83.
[5]	LARSON KM, SMALL E E, GUTMANN E D, et al. Using GPS multipath to measure soil moisture fluctuations: initial results[J]. GPS solutions, 2008, 12(3): 173-177. DOI: 10.1007/s10291-007-0076-6
[6]	NIEVINSKI F G, LARSON K M. An open source GPS multipath simulator in Matlab/Octave[J]. GPS solutions, 2014, 18(3): 473-481. DOI: 10.1007/s10291-014-0370-z
[7]	ROESLER C, LARSON K M. Software tools for GNSS interferometric reflectometry (GNSS-IR)[J]. GPS solutions, 2018, 22(3): 1-10. DOI: 10.1007/s10291-018-0744-8
[8]	LARSON K M. Kristine’s GNSS-IR WebApp[J/OL].(2020-06-27)[2023-01-10]. https://www.kristinelarson.net/kristines-web-app/
[9]	张勤. GPS原理及应用[M]. 北京: 科学出版社; 2005.
[10]	张双成, 戴凯阳, 刘凯, 等. GPS-MR技术用于降雪厚度监测研究[J]. 地球物理学进展. 2016, 31(4): 1879-1884.
[11]	LARSON K M. Unanticipated uses of the global positioning system[J]. Annual review of earth and planetary sciences, 2019, 47(1): 19-40. DOI: 10.1146/annurev-earth-053018-060203
[12]	MARTIN-NEIRA M. A passive reflectometry and interferometry system (PARIS): application to ocean altimetry[J]. ESA journal, 1993, 17(4): 331-355.
[13]	KAVAK A, VOGEL W J, XU G H. Using GPS to measure ground complex permittivity[J]. Electronics letters, 1998, 34(3): 254. DOI: 10.1049/el:19980180
[14]	ENTEKHABI D, RODRIGUEZ-ITURBE I. Analytical framework for the characterization of the space-time variability of soil moisture[J]. Advances in water resources, 1994, 17(1-2): 35-45. DOI: 10.1016/0309-1708(94)90022-1
[15]	VITERBO P, BETTS A K. Impact of the ECMWF reanalysis soil water on forecasts of the July 1993 Mississippi flood[J]. Journal of geophysical research atmospheres, 1999, 104(D16): 19361-19366. DOI: 10.1029/1999JD900449
[16]	HIRSCHI M, SENEVIRATNE S I, ALEXANDROV V, et al. Observational evidence for soil-moisture impact on hot extremes in southeastern Europe[J]. Nature geoscience, 2011(4): 17-21. DOI: 10.1038/NGEO1032
[17]	ZENG J Y, CHEN K S, BI H Y, et al. A preliminary evaluation of the SMAP radiometer soil moisture product over united states and Europe using ground-based measurements[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(8): 4929-4940. DOI: 10.1109/TGRS.2016.2553085
[18]	BROCCA L, MELONE F, MORAMARCO T, et al. Improving runoff prediction through the assimilation of the ASCAT soil moisture product[J]. Hydrology and earth system sciences, 2010, 14(10): 1881-1893. DOI: 10.5194/hess-14-1881-2010
[19]	SCHAUFLER G, KITZLER B, SCHINDLBACHER A, et al. Greenhouse gas emissions from European soils under different land use: effects of soil moisture and temperature[J]. European journal of soil science, 2010, 61(5): 683-696. DOI: 10.1111/j.1365-2389.2010.01277.x
[20]	LARSON K M, SMALL E E, GUTMANN E D, et al. Use of GPS receivers as a soil moisture network for water cycle studies[J]. Geophysical research letters, 2008, 35(24). DOI: 10.1029/2008GL036013
[21]	CHEW C C, SMALL E E, LARSON K M, et al. Effects of near-surface soil moisture on GPS SNR data: development of a retrieval algorithm for soil moisture[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(1): 537-543. DOI: 10.1109/TGRS.2013.2242332
[22]	CHEW C, SMALL E E, LARSON K M, et al. An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil[J]. GPS solutions, 2016, 20(3): 525-537. DOI: 10.1007/s10291-015-0462-4
[23]	SMALL E E, LARSON K M, CHEW C C, et al. Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 9(10): 4759-4770. DOI: 10.1109/JSTARS.2015.2504527
[24]	VEY S, GUNTNER A, WICKERT J, et al. Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa[J]. GPS solutions, 2016, 20(4): 641-654. DOI: 10.1007/s10291-015-0474-0
[25]	YANG T, WAN W, CHEN X, et al. Using BDS SNR observations to measure near-surface soil moisture fluctuations: results from low vegetated surface[J]. IEEE geoscience and remote sensing letters, 2017, 14(8): 1308-1312. DOI: 10.1109/LGRS.2017.2710083
[26]	SHI Y J, REN C, YAN Z H, et al. High spatial-temporal resolution estimation of Ground-Based global navigation satellite system interferometric reflectometry (GNSS-IR) soil moisture using the genetic algorithm back propagation (GA-BP) neural network[J]. ISPRS international journal of geo-information, 2021, 10(9): 623. DOI: 10.3390/ijgi10090623
[27]	LARSON K M, NIEVINSKI F G. GPS snow sensing: results from the earthscope plate boundary observatory[J]. GPS solutions, 2013, 17(1): 41-52. DOI: 10.1007/s10291-012-0259-7
[28]	RAN Q S, ZHANG B, YAO Y B, et al. Editing arcs to improve the capacity of GNSS-IR for soil moisture retrieval in undulating terrains[J]. GPS solutions, 2022, 26(1): 1-11. DOI: 10.1007/s10291-021-01206-y
[29]	NIE S S, WANG Y X, TU J S, et al. Retrieval of soil moisture content based on multisatellite dual-frequency combination multipath errors[J]. Remote sensing, 2022, 14(13): 3193. DOI: 10.3390/rs14133193
[30]	ZHOU X, ZHANG S C, ZHANG Q, et al. Research of deformation and soil moisture in loess landslide simultaneous retrieved with ground-based GNSS[J]. Remote sensing, 2022, 14(22): 5687. DOI: 10.3390/rs14225687
[31]	SMALL E E, LARSON K M, BRAUN J J. Sensing vegetation growth with reflected GPS signals[J]. Geophysical research letters, 2010, 37(12): 1-5. DOI: 10.1029/2010GL042951
[32]	FERRAZZOLI P, GUERRIERO L, PIERDICCA N, et al. Forest biomass monitoring with GNSS-R: theoretical simulations[J]. Advances in space research, 2011, 47(10): 1823-1832. DOI: 10.1016/j.asr.2010.04.025
[33]	RODRIGUEZ-ALVAREZ N, BOSCH-LLUIS X, CAMPS A, et al. Vegetation water content estimation using GNSS measurements[J]. IEEE geoscience and remote sensing letters, 2012, 9(2): 282-286. DOI: 10.1109/LGRS.2011.2166242
[34]	EGIDO A, CAPARRINI M, RUFFINI G, et al. Global navigation satellite systems reflectometry as a remote sensing tool for agriculture[J]. Remote sensing, 2012, 4(8): 2356-2372. DOI: 10.3390/rs4082356
[35]	LARSON K M, SMALL E E. Normalized microwave reflection index: a vegetation measurement derived from GPS data[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2014, 7(5): 1501-1511. DOI: 10.1109/JSTARS.2014.2300116
[36]	SMALL E E, LARSON K M, SMITH W K. Normalized microwave reflection index: validation of vegetation water content estimates at montana grasslands[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2014, 7(5): 1512-1521. DOI: 10.1109/JSTARS.2014.2320597
[37]	EVANS S G, SMALL E E, LARSON K M. Comparison of vegetation phenology in the western united states from reflected GPS microwave signals and NDVI[J]. International journal of remote sensing, 2014, 35(9): 2996-3017. DOI: 10.1080/01431161.2014.894660
[38]	JONES M O, KIMBALL J S, SMALL E E, et al. Comparing land surface phenology derived from satellite and GPS network microwave remote sensing[J]. International journal of biometeorology, 2014, 58(6): 1305-1315. DOI: 10.1007/s00484-013-0726-z
[39]	CHEW C C, SMALL E E, LARSON K M, et al. Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data[J]. IEEE transactions on geoscience and remote sensing, 2015, 53(5): 2755-2764. DOI: 10.1109/TGRS.2014.2364513
[40]	WAN W, LARSON K M, SMALL E E, et al. Using geodetic GPS receivers to measure vegetation water content[J]. GPS solutions, 2015, 19(2): 237-248. DOI: 10.1007/s10291-014-0383-7
[41]	WU X R, JIN S G, XIA J M. A Forward GPS multipath simulator based on the vegetation radiative transfer equation model[J]. Sensors, 2017, 17(6): 1291. DOI: 10.3390/s17061291
[42]	吴继忠, 吴玮. 基于GPS-IR的美国中西部地区NDVI时间序列反演[J]. 农业工程学报, 2016, 32(24): 183-188. DOI: 10.11975/j.issn.1002-6819.2016.24.024
[43]	祁云, 郑南山, 严薛峰, 等. 利用GPS的信噪比观测值监测植被生物量[J]. 科技技术与工程, 2018, 18(1): 177-181.
[44]	YANG T, WAN W, CHEN X W, et al. Land surface characterization using BeiDou signal to-noise ratio observations[J]. GPS solutions, 2019, 32(2): 1-12. DOI: 10.1007/s10291-019-0824-4
[45]	ZHANG S C, WANG T, WANG L X, et al. Evaluation of GNSS-IR for retrieving soil moisture and vegetation growth characteristics in wheat farmland[J]. Journal of surveying engineering, 2021, 147(3): 04021009. DOI: 10.1061/(ASCE)SU.1943-5428.0000355
[46]	ZHAN J Y, ZHANG R, XIE L, et al. Vegetation growth monitoring based on BDS interferometry reflectometry with triple-frequency SNR data[J]. IEEE geoscience and remote sensing letters, 2022(19): 1-5. DOI: 10.1109/LGRS.2022.3204579
[47]	Armstrong R L, BRODZIK M J. Recent northern hemisphere snow extent: a comparison of data derived from visible and microwave satellite sensors[J]. Geophysical research letters, 2001, 28(19): 3673-3676. DOI: 10.1029/2000GL012556
[48]	FREI A, ROBINSON D A. Northern hemisphere snow extent: regional variability 1972–1994[J]. International journal of climatology:a journal of the royal meteorological society, 1999, 19(14): 1535-1560. DOI: 10.1002/(SICI)1097-0088(19991130)19:14<1535::AID-JOC438>3.0.CO;2-J
[49]	ROBINSON D A, KENNETH F D, RICHARD R H J. Global snow cover monitoring: an update[J]. Bulletin of the American meteorological society, 1993, 74(9): 1689-1696. DOI: 10.1175/1520-0477(1993)074<1689:GSCMAU>2.0.CO;2
[50]	HENDERSON G R, PEINGS Y, FURTADO J C, et al. Snow–atmosphere coupling in the northern hemisphere[J]. Nature climate change, 2018, 8(D3): 954-963. DOI: 10.1038/s41558-018-0295-6
[51]	LARSON K M, GUTMANN E, ZAVOROTNY V U, et al. Can we measure snow depth with GPS receivers[J]. Geophysical research letters, 2009, 36(17): L17502. DOI: 10.1029/2009GL039430
[52]	OZEIK M, HEKI K. GPS snow depth meter with geometry-free linear combinations of carrier phases[J]. Journal of geodesy, 2012, 86(3): 209-219. DOI: 10.1007/s00190-011-0511-x
[53]	YU K G, BAN W, ZHANG X H, et al. Snow depth estimation based on multipath phase combination of GPS triple-frequency signals[J]. IEEE transactions on geoscience and remote sensing, 2015, 53(9): 5100-5109. DOI: 10.1109/TGRS.2015.2417214
[54]	ZHANG Z Y, GUO F, ZHANG X H. Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation[J]. GPS solutions, 2020, 24(2): 1-18. DOI: 10.1007/s10291-020-0966-4
[55]	TABIBI S, GEREMIA-NIEVINSKI F, VAN D T. Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval[J]. IEEE transactions on geoscience and remote sensing, 2017, 55(7): 3773-3785. DOI: 10.1109/TGRS.2017.2679899
[56]	ZHANG S C, WANG X L, ZHANG Q. Avoiding errors attributable to topography in GPS-IR snow depth retrievals[J]. Advances in space research, 2017, 59(6): 1663-1669. DOI: 10.1016/j.asr.2016.12.031
[57]	HU Y, YUAN X T, LIU W, et al. GNSS-R snow depth inversion based on variational mode decomposition with multi-GNSS constellations[J]. IEEE transactions on geoscience and remote sensing, 2022(60): 1-12. DOI: 10.1109/TGRS.2022.3182987
[58]	HU Y, YUAN X T, LIU W, et al. An SVM-based snow detection algorithm for GNSS-R snow depth retrievals[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2022(15): 6046-6052. DOI: 10.1109/JSTARS.2022.3193113
[59]	WANG X L, ZHANG S C, WANG L F, et al. Analysis and combination of multi-GNSS snow depth retrievals in multipath reflectometry[J]. GPS solutions, 2020, 24(3): 1-13. DOI: 10.1007/s10291-020-00990-3
[60]	LI Z, CHEN P, ZHENG N Q, et al. Accuracy analysis of GNSS-IR snow depth inversion algorithms[J]. Advances in space research, 2021, 67(4): 1317-1332. DOI: 10.1016/j.asr.2020.11.02
[61]	WAN W, ZHANG J, DAI L Y, et al. A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1.0, 2013–2022)[J]. Earth system science data, 2022, 14(8): 3549-3571. DOI: 10.5194/essd-14-3549-2022
[62]	ZHANG S, PENG J, ZHANG C L, et al. GiRsnow: an open-source software for snow depth retrievals using GNSS interferometric reflectometry[J]. GPS solutions, 2021, 25(2): 1-8. DOI: 10.1007/s10291-021-01096-0
[63]	ANDERSON K D. Determination of water level and tides using interferometric observations of GPS signals[J]. Journal of atmospheric and oceanic technology, 2000, 17(8): 1118-1127. DOI: 10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2
[64]	LARSON K M, LOFGREN J S, HASS R. Coastal sea level measurements using a single geodetic GPS receiver[J]. Advances in space research, 2013, 51(8): 1301-1310. DOI: 10.1016/j.asr.2012.04.017
[65]	LARSON K M, RAY R D, NIEVINSKI F G, et al. The accidental tide gauge: a GPS reflection case study from Kachemak Bay, Alaska[J]. IEEE geoscience and remote sensing letters, 2013, 10(5): 1200-1204. DOI: 10.1109/LGRS.2012.2236075
[66]	LOFGREN J S, HAAS R, SCHERNECK H-G. Sea level time series and ocean tide analysis from multipath signals at five GPS sites in different parts of the world[J]. Journal of geodynamics, 2014(80): 66-80. DOI: 10.1016/j.jog.2014.02.012
[67]	LARSON K M, RAY R D, WILLIAMS S D P. A ten-year comparison of water levels measured with a geodetic GPS receiver versus a conventional tide gauge[J]. Journal of atmospheric and oceanic technology, 2017, 34(2): 295-307. DOI: 10.1175/JTECH-D-16-0101.1
[68]	ROUSSEL N, RAMILLIEN G, FRAPPART F, et al. Sea level monitoring and sea state estimate using a single geodetic receiver[J]. Remote sensing of environment, 2015, 171(15): 261-277. DOI: 10.1016/j.rse.2015.10.011
[69]	STRANDBERG J, HOBIGER T, HASS R. Improving GNSS-R sea level determination through inverse modeling of SNR data[J]. Radio science, 2016, 51(8): 1286-1296. DOI: 10.1002/2016RS006057
[70]	STRANDBERG J, HOBIGER T, HASS R. Real-time sea-level monitoring using Kalman filtering of GNSS-R data[J]. GPS solutions, 2019, 23(3): 61. DOI: 10.1007/s10291-019-0851-1
[71]	REINKING J. GNSS-SNR water level estimation using global optimization based on interval analysis[J]. Journal of geodetic science, 2016, 6(1). DOI: 10.1515/jogs-2016-0006
[72]	PURNELL D, GOMEZ N, CHAN N H, et al. Quantifying the uncertainty in ground-based GNSS-reflectometry sea level measurements[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2020(13): 4419-4428. DOI: 10.1109/JSTARS.2020.3010413
[73]	LIU Q, ZHANG S C. An improved sea level retrieval method using the differential evolution of GNSS SNR data[J]. Advances in space research, 2021, 67(3): 975-984. DOI: 10.1016/j.asr.2020.10.050
[74]	ROUSSELl N, FRAPPART F, RAMILLIEN G, et al. Simulations of direct and reflected wave trajectories for ground-based GNSS-R experiments[J]. Geoscientific model development, 2014, 7(5): 2261-2279. DOI: 10.5194/gmd-7-2261-2014
[75]	SANTAMARIA-GOMEZ A, WATSON C. Remote leveling of tide gauges using GNSS reflectometry: case study at spring bay, Australia[J]. GPS solutions, 2017, 21(2): 451-459. DOI: 10.1007/s10291-016-0537-x
[76]	WILLIAMS S D P, NIEVINSKI F G. Tropospheric delays in ground‐based GNSS multipath reflectometry—experimental evidence from coastal sites[J]. Journal of geophysical research:solid earth, 2017, 122(3): 2310-2327. DOI: 10.1002/2016JB013612
[77]	JIN S G, QIAN X D, WU X. Sea level change from BeiDou Navigation Satellite System-Reflectometry (BDS-R): first results and evaluation[J]. Global and planetary change, 2017(149): 20-25. DOI: 10.1016/j.gloplacha.2016.12.010
[78]	LIU Z, DU L, ZHOU P Y, et al. BDS/GNSS multipath reflectometry (BDS/GNSS-MR) based altimetry with new signals: initial assessment and comparison[J]. Advances in space research, 2022, 69(1): 282-291. DOI: 10.1016/j.asr.2021.08.025
[79]	WANG X L, HE X F, ZHANG Q. Evaluation and combination of quad-constellation multi-GNSS multipath reflectometry applied to sea level retrieval[J]. Remote sensing of environment, 2019, 231(2): 111229. DOI: 10.1016/j.rse.2019.111229
[80]	WANG X L, HE X F, XIAO R Y, et al. Millimeter to centimeter scale precision water-level monitoring using GNSS reflectometry: application to the south-to-north water diversion project, China[J]. Remote sensing of environment, 2021, 265(8): 112645. DOI: 10.1016/j.rse.2021.112645
[81]	WANG X L, HE X F, ZHANG Q. Coherent superposition of multi-GNSS wavelet analysis periodogram for sea-level retrieval in GNSS multipath reflectometry[J]. Advances in space research, 2020, 65(7): 1781-1788. DOI: 10.1016/j.asr.2019.12.023
[82]	张双成, 武慧琳, 张化疑, 等. 中国沿海 GPS 站用于潮波系数提取分析[J]. 海洋测绘, 2019, 39(3): 1-5. DOI: 10.3969/j.issn.1671-3044.2019.03.001
[83]	TABIBI S, GEREMIA-NIEVINSKI F, FRANCIS O, et al. Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland[J]. Remote sensing of environment, 2020(248): 111959. DOI: 10.1016/j.rse.2020.111959
[84]	武慧琳. 岸基 GNSS 解译潮位及潮波系数提取研究[D]. 西安: 长安大学, 2020.
[85]	PENG D J, HILL E M, LI L L, et al. Application of GNSS interferometric reflectometry for detecting storm surges[J]. GPS solutions, 2019, 23(2): 47. DOI: 10.1007/s10291-019-0838-y
[86]	何秀凤, 王杰, 王笑蕾, 等. 利用多模多频 GNSS-IR 信号反演沿海台风风暴潮[J]. 测绘学报, 2020, 49(9): 1168-1178.
[87]	HOLDEN L D, LARSON K M. Ten years of lake taupō surface height estimates using the GNSS interferometric reflectometry[J]. Journal of geodesy, 2021, 95(7): 74. DOI: 10.1007/s00190-021-01523-7
[88]	LESTARQUIT L, PEYREZABES M, DARROZES J, et al. Reflectometry with an open-source software GNSS receiver: use case with carrier phase altimetry[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 9(10): 4843-4853. DOI: 10.1109/JSTARS.2016.2568742
[89]	LI W Q, CARDELLACH E, FABRA F, et al. Lake level and surface topography measured with spaceborne GNSS‐reflectometry from CYGNSS mission: example for the lake Qinghai[J]. Geophysical research letters, 2018, 45(24): 13332-13341. DOI: 10.1029/2018GL080976
[90]	XU L W, WAN W, CHEN X W, et al. Spaceborne GNSS-R observation of global lake level: first results from the TechDemoSat-1 mission[J]. Remote sensing, 2019, 11(12): 1438. DOI: 10.3390/rs11121438
[91]	ZEIGER P, FRAPPART F, DARROZES J, et al. SNR-based water height retrieval in rivers: application to high amplitude asymmetric tides in the garonne river[J]. Remote sensing, 2021, 13(9): 1856. DOI: 10.3390/rs13091856
[92]	VU P L, FRAPPART F, DARROZES J, et al. Comparison of water level changes in the mekong river using GNSS reflectometry, satellite altimetry and in-situ tide/river Gauges[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018.
[93]	HA M C. Evolution of soil moisture and analysis of fluvial altimetry using GNSS-R[D]. Université Paul Sabatier-Toulouse III, 2018.
[94]	SONG M, HE X F, WANG X L, et al. Study on the quality control for periodogram in the determination of water level using the GNSS-IR technique[J]. Sensors, 2019, 19(20): 4524. DOI: 10.3390/s19204524