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李婷，张显云，邓小东，李宏达，聂士海.2021.顾及多径环境差异和粗差的GNSS-MR土壤湿度反演.遥感学报，25（6）： 1324-1337
Li T，Zhang X Y，Deng X D，Li H D and Nie S H. 2021. GNSS-MR soil moisture retrieval considering the multipath environments differences and gross error. National Remote Sensing Bulletin， 25（6）：1324-1337
针对基于单系统单卫星GNSS-MR（GNSS Multipath Reflectometry）土壤湿度反演的可靠性不高、实际可操作性不强和最小二乘估计不具鲁棒性的缺点，为获取更优的延迟相位估值，并改善GNSS-MR土壤湿度反演的可靠性和实际可操作性，同时简化繁杂的选星过程，提出了一种基于抗差估计的多系统多卫星组合GNSS-MR土壤湿度反演算法。该算法首先顾及多径环境的差异性、多径误差的周期特性等进行信噪比SNR（Signal to Noise Ratio）观测值的筛选，然后采用基于IGGIII（Weight Function III Developed by Institute of Geodesy and Geophysics）权函数的抗差估计解求延迟相位，进而获得表征土壤湿度变化趋势的延迟相位组合。实验结果表明，相较于未采用抗差估计的多系统多卫星组合（方案1）和单卫星组合（方案3），得益于抗差估计良好的鲁棒性，基于抗差估计的多系统多卫星组合（方案2）和单卫星组合（方案4）获得了较高的建模精度，所得延迟相位与实测土壤湿度间的相关系数分别为0.97和0.95、土壤湿度拟合残差的均方根误差分别为0.010和0.012；同时，方案2和方案4还取得了较高的土壤湿度预报精度，土壤湿度预测值与土壤湿度实测值间的相关系数分别为0.92和0.91、土壤湿度预报残差的均方根误差分别为0.016和0.023；此外，相比于方案4，方案2在采用抗差估计解求延迟相位的基础上，采用多系统多卫星组合进一步提升了延迟相位的估值精度，从而不仅避免了复杂的选星过程，而且还获得了更好的建模效果和更高的土壤湿度预报精度。
In order to obtain better phase delay estimation
improve the reliability and the practical operability of GNSS-MR(GNSS Multipath Reflectometry) soil moisture inversion
and also to simplify the complex process of the satellite selection
a multi-system multi-satellite GNSS-MR soil moisture inversion algorithm based on the robust estimation was proposed in view of the poor reliability and operability of the single system single satellite GNSS-MR soil moisture inversion and the least squares estimation of no robustness. In this algorithm
the spatial difference of multipath environment and the periodic characteristics of multipath were taken into account to screen the SNR (Signal to Noise Ratio) observations. Then
the phase delay combination representing the change trend of soil moisture was obtained by using the robust estimation based on IGGIII (Weight Function III Developed by Institute of Geodesy and Geophysics) weight function. Compared with multi-system multi- satellite combination (scheme 1) and the single-satellite combination (scheme 3)
the experimental results showed that the multi-system multi-satellite combination (scheme 2) and the single-satellite combination (scheme 4) based on the robust estimation achieved higher modeling accuracy
which were benefited from the positive performance of the robust estimation. The correlation coefficients between the estimated phase delays and the measured soil moisture were 0.97 and 0.95
respectively
and the root mean square error of the soil moisture fitting residual were 0.010 and 0.012
respectively. At the same time
scheme 2 and scheme 4 also achieved higher soil moisture prediction accuracy
with the correlation coefficient between the predicted soil moisture and the measured soil moisture being 0.92 and 0.91
respectively
and the root mean square error of the soil moisture forecast residuals being 0.016 and 0.023
respectively. In addition
compared with scheme 4
scheme 2 not only adopted the robust estimation
but also adopted the multi-system multi-satellite combination
which contributed to better modeling effect and higher modeling accuracy. Moreover
because it could avoid the complex process of the satellite selection
scheme 2 owned better performance in GNSS-MR soil moisture inversion.
多系统多卫星GNSS-MR土壤湿度反演信噪比延迟相位抗差估计
multi-system multi-satellite GNSS-MRsoil moisture inversionsignal to noise ratiodelayed phaserobust estimation
Ao M S, Hu Y J, Liu Y D, Zeng Y and Wu Z. 2012. Inversion of soil moisture fluctuation based on signal-to-noise ratio of global positioning system. Journal of Geomatics Science and Technology, 29(2): 140-143
敖敏思, 胡友健, 刘亚东, 曾云, 吴正. 2012. GPS信噪比观测值的土壤湿度变化趋势反演. 测绘科学技术学报, 29(2): 140-143 [DOI: 10.3969/j.issn.1673-6338.2012.02.015http://dx.doi.org/10.3969/j.issn.1673-6338.2012.02.015]
Ao M S, Zhu J J, Hu Y J, Zeng Y and Liu Y D. 2015. Comparative experiments on soil moisture monitoring with GPS SNR observations. Geomatics and Information Science of Wuhan University, 40(1): 117-120, 127
敖敏思, 朱建军, 胡友健, 曾云, 刘亚东. 2015. 利用SNR观测值进行GPS土壤湿度监测. 武汉大学学报(信息科学版), 40(1): 117-120, 127 [DOI: 10.13203/j.whugis20130170http://dx.doi.org/10.13203/j.whugis20130170]
Brocca L, Melone F, Moramarco T, Wagner W, Naeimi V, Bartalis Z and Hasenauer S. 2010. Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences, 14(10): 1881-1893 [DOI: 10.5194/hess-14-1881-2010http://dx.doi.org/10.5194/hess-14-1881-2010]
Chen F D, Liu L L, Huang L K and Li J Y. 2018. Sea surface height monitoring based on Multi-GNSS-MR. Progress in Geophysics, 33(5): 1767-1772
陈发德, 刘立龙, 黄良珂, 黎峻宇. 2018. 基于多模GNSS-MR海平面测高研究. 地球物理学进展, 33(5): 1767-1772 [DOI: 10.6038/pg2018BB0412http://dx.doi.org/10.6038/pg2018BB0412]
Chew C C, Small E E, Larson K M and Zavorotny V U. 2015. Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of Canopy parameters on Signal-to-Noise ratio data. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2755-2764 [DOI: 10.1109/tgrs.2014.2364513http://dx.doi.org/10.1109/tgrs.2014.2364513]
Jin S G, Zhang Q Y and Qian X D. 2017. New progress and application prospects of Global Navigation Satellite System Reflectometry (GNSS+R). Acta Geodaetica et Cartographica Sinica, 46(10): 1389-1398
金双根, 张勤耘, 钱晓东. 2017. 全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景. 测绘学报, 46(10): 1389-1398 [DOI: 10.11947/j.AGCS.2017.20170282http://dx.doi.org/10.11947/j.AGCS.2017.20170282]
Kerr Y H, Waldteufel P, Wigneron J P, Delwart S, Cabot F, Boutin J, Escorihuela M J, Font J, Reul N, Gruhier C, Juglea S E, Drinkwater M R, Hahne A, Martín-Neira M and Mecklenburg S. 2010. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5): 666-687. [DOI: 10.1109/jproc.2010.2043032http://dx.doi.org/10.1109/jproc.2010.2043032]
Larson K M, Braun J J, Small E E, Zavorotny V U, Gutmann E D and Bilich A L. 2010. GPS multipath and its relation to near-surface soil moisture content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 91-99 [DOI: 10.1109/jstars.2009.2033612http://dx.doi.org/10.1109/jstars.2009.2033612]
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 [DOI: 10.1007/s10291-007-0076-6http://dx.doi.org/10.1007/s10291-007-0076-6]
Ma J G, Cao K J, Zhang L and Li B. 2011. Passive detection of marine targets based on GPS signals reflected from sea surface. Systems Engineering and Electronics, 33(5): 987-991
马建国, 曹可劲, 张磊, 李豹. 2011. 基于GPS信号的海面目标被动探测. 系统工程与电子技术, 33(5): 987-991 [DOI: 10.3969/j.issn.1001-506X.2011.05.05http://dx.doi.org/10.3969/j.issn.1001-506X.2011.05.05]
Ray R L and Jacobs J M. 2007. Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards, 43(2): 211-222 [DOI: 10.1007/s11069-006-9095-9http://dx.doi.org/10.1007/s11069-006-9095-9]
Robock A, Vinnikov K Y, Srinivasan G, Entin J K, Hollinger S E, Speranskaya N A, Liu S X and Namkhai A. 2000. The global soil moisture data bank. Bulletin of the American Meteorological Society, 81(6): 1281-1300 [DOI: 10.1175/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2http://dx.doi.org/10.1175/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2]
Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F and Ha C. 2015. Detection of soil moisture content changes by using a single geodetic antenna: the case of an agricultural plot//2015 IEEE International Geoscience and Remote Sensing Symposium. Milan: IEEE: 2008-2011 [DOI: 10.1109/igarss.2015.7326192http://dx.doi.org/10.1109/igarss.2015.7326192]
Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F, Lestarquit L and Ha M C. 2016. Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2° to 70°. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10): 4781-4794 [DOI: 10.1109/jstars.2016.2537847http://dx.doi.org/10.1109/jstars.2016.2537847]
Schaufler G, Kitzler B, Schindlbacher A, Skiba U, Sutton M A and Zechmeister-Boltenstern S. 2010. Greenhouse gas emissions from European soils under different land use: effects of soil moisture and temperature. European Journal of Soil Science, 61(5): 683-696 [DOI: 10.1111/j.1365-2389.2010.01277.xhttp://dx.doi.org/10.1111/j.1365-2389.2010.01277.x]
Seneviratne S I, Corti T, Davin E L, Hirschi M, Jaeger E B, Lehner L, Orlowsky B and Teuling A J. 2010. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Science Reviews, 99(3/4): 125-161 [DOI: 10.1016/j.earscirev.2010.02.004http://dx.doi.org/10.1016/j.earscirev.2010.02.004]
Seuffert G, Wilker H, Viterbo P, Mahfouf J F, Drusch M and Calvet J C. 2003. Soil moisture analysis combining screen‐level parameters and microwave brightness temperature: a test with field data. Geophysical Research Letters, 30(10): 1498 [DOI: 10.1029/2003GL017128http://dx.doi.org/10.1029/2003GL017128]
Strode P R R and Groves P D. 2016. GNSS multipath detection using three-frequency signal-to-noise measurements. GPS Solutions, 20(3): 399-412 [DOI: 10.1007/s10291-015-0449-1http://dx.doi.org/10.1007/s10291-015-0449-1]
Tabibi S, Geremia-Nievinski F and Van Dam T. 2017. Statistical comparison and combination of GPS, GLONASS, and Multi-GNSS multipath reflectometry applied to snow depth retrieval. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3773-3785 [DOI: 10.1109/tgrs.2017.2679899http://dx.doi.org/10.1109/tgrs.2017.2679899]
Vey S, Güntner A, Wickert J, Blume T and Ramatschi M. 2016. Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solutions, 20(4): 641-654 [DOI: 10.1007/s10291-015-0474-0http://dx.doi.org/10.1007/s10291-015-0474-0]
Wan W, Chen X W, Peng X F, Bai W H, Xia J M, Liang H, Zhang X M, Xiong P, Yang T, Cao Y C, Yi C, Zhao L M and Hong Y. 2016. Overview and outlook of GNSS remote sensing technology and applications. Journal of Remote Sensing, 20(5): 858-874
万玮, 陈秀万, 彭学峰, 白伟华, 夏俊明, 梁宏, 张学民, 熊攀, 杨婷, 曹云昌, 尹聪, 赵利民, 洪阳. 2016. GNSS遥感研究与应用进展和展望. 遥感学报, 20(5): 858-874 [DOI: 10.11834/jrs.20166228http://dx.doi.org/10.11834/jrs.20166228]
Wan W, Larson K M, Small E E, Chew C C and Braun J J. 2015. Using geodetic GPS receivers to measure vegetation water content. GPS Solutions, 19(2): 237-248 [DOI: 10.1007/s10291-014-0383-7http://dx.doi.org/10.1007/s10291-014-0383-7]
Wanders N, Karssenberg D, De Roo A, De Jong S M and Bierkens M F P. 2014. The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrology and Earth System Sciences Discussions, 10(11): 13783-13816 [DOI: 10.5194/hessd-10-13783-2013http://dx.doi.org/10.5194/hessd-10-13783-2013]
Wang Z M, Liu Z K, An J C and Lin G B. 2018. Snow depth detection and error analysis derived from SNR of GPS and BDS. Acta Geodaetica et Cartographica Sinica, 47(1): 8-16
王泽民, 刘智康, 安家春, 林国标. 2018. 基于GPS和北斗信噪比观测值的雪深反演及其误差分析. 测绘学报, 47(1): 8-16 [DOI: 10.11947/j.AGCS.2018.20160644http://dx.doi.org/10.11947/j.AGCS.2018.20160644]
Wu D, Wang L, Zhang Q, Fu W J, Shu B and Fan L H. 2015. Implementation and verification analysis of the software of GNSS data quality check. Journal of Geomatics Science and Technology, 32(4): 344-348
吴丹, 王利, 张勤, 付文举, 舒宝, 范丽红. 2015. GNSS数据质量评估软件的实现及验证分析. 测绘科学技术学报, 32(4): 344-348 [DOI: 10.3969/j.issn.1673-6338.2015.04.004http://dx.doi.org/10.3969/j.issn.1673-6338.2015.04.004]
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, 31(6): 702-705
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, 31(6): 702-705 [DOI: 10.3969/j.issn.0258-0934.2011.06.026http://dx.doi.org/10.3969/j.issn.0258-0934.2011.06.026]
Yang T, Wan W, Chen X W, Chu T X and Hong Y. 2017. Using BDS SNR observations to measure near-surface soil moisture fluctuations: results from low vegetated surface. IEEE Geoscience and Remote Sensing Letters, 14(8): 1308-1312 [DOI: 10.1109/lgrs.2017.2710083http://dx.doi.org/10.1109/lgrs.2017.2710083]
Yang Y X. 1993. Robust Estimation and Its Applications. Beijing: Bayi Press
杨元喜. 1993. 抗差估计理论及其应用. 北京: 八一出版社
Ye X F. 2016. Research on Processing Method of GNSS Station Environment Error and Its Application Using SNR Data. Wuhan: China University of Geosciences (Wuhan): 75-88
叶险峰. 2016. 基于GNSS信噪比数据的测站环境误差处理方法及其应用研究. 武汉: 中国地质大学(武汉): 75-88
Yin Z M, Meng F Y, Chen M J, Yan J Q and Wang W. 2016. Quality analysis of satellite observation. GNSS World of China, 41(1): 54-59
尹子明, 孟凡玉, 陈明剑, 闫建巧, 汪威. 2016. 卫星导航数据质量分析. 全球定位系统, 41(1): 54-59 [DOI: 10.13442/j.gnss.1008-9268.2016.01.010http://dx.doi.org/10.13442/j.gnss.1008-9268.2016.01.010]
Yu K G, Ban W, Zhang X H and Yu X W. 2015. Snow depth estimation based on multipath phase combination of GPS triple-frequency signals. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 5100-5109 [DOI: 10.1109/tgrs.2015.2417214http://dx.doi.org/10.1109/tgrs.2015.2417214]
Zavorotny V U, Larson L M, Braun J J, Small E E, Gutmann E D and Bilich A L. 2010. A physical model for GPS multipath caused by land reflections: toward bare soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 100-110 [DOI: 10.1109/jstars.2009.2033608http://dx.doi.org/10.1109/jstars.2009.2033608]
Zhang S C, Nan Y, Li Z Y, Zhang Q, Dai K Y and Zhao Y H. 2016. Analysis of tide variation monitored by GNSS-MR. Acta Geodaetica et Cartographica Sinica, 45(9): 1042-1049
张双成, 南阳, 李振宇, 张勤, 戴凯阳, 赵迎辉. 2016. GNSS-MR技术用于潮位变化监测分析. 测绘学报, 45(9): 1042-1049 [DOI: 10.11947/j.AGCS.2016.20150498http://dx.doi.org/10.11947/j.AGCS.2016.20150498]
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