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摘要:
对利用北斗导航系统地球静止轨道（GEO）卫星干涉信号功率反演土壤湿度的方法进行研究。针对现有研究主要建立纯经验模型反演土壤湿度、反演方法单一的问题，提出一种半经验的反演方法。所提方法针对北斗GEO卫星星地之间几何构型较为稳定的特点，利用相邻两天的干涉幅度信息抵消发射信号功率的影响，实现了土壤反射系数的反演。基于交叉极化影响下的反射系数理论模型构建了一种半经验的土壤湿度反演模型，通过仿真与实验对所提方法和模型进行验证。仿真结果表明：所提方法和模型能更好地适应反演过程中出现的非线性情况。实验结果表明：土壤湿度反演的均方根误差小于0.02 cm3/cm3，相关系数超过0.8。
Abstract:Soil moisture retrieval using Beidou geostationary Earth orbit (GEO) interference signal power was studied. Current researches mainly establish empirical models for retrieving soil moisture. Therefore, a semi-empirical retrieval method was proposed. Taking the advantage of Beidou GEO satellite’s stable geometric configuration relative to the earth, this method utilized the interference amplitude metric obtained in two consecutive days to cancel the influence of the power of the transmitted signal, therefore, achieving the retrieval of the reflection coefficient. Then, based on the reflection coefficient theoretical model under the influence of cross-polarization , a semi-empirical soil moisture retrieval model was constructed. Finally, the proposed method and model are validated through simulations and experiments. The simulation results showed that the proposed method and model can better adapt to the nonlinear situation encountered in the retrieval process. Finally, the experiment results show that the root mean square error of soil moisture retrieval is less than 0.02 cm3 ·cm−3, and the correlation coefficient exceeds 0.8.
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图 2 RHCP与LHCP分量反射系数模值
Figure 2. Modulus of RHCP and LHCP component reflection coefficient.
图 4 交叉极化影响下的GEO卫星载噪比仿真
Figure 4. Simulation of GEO satellite C/N0 under effect of cross-polarization
图 5 无交叉极化影响下的GEO卫星载噪比仿真
Figure 5. Simulation of GEO satellite C/N0 without effect of cross-polarization
图 11 4号星的反射系数反演结果
Figure 11. Retrieval results of reflection coefficient for satellite No.4
图 14 不同训练数据量下的反演性能
Figure 14. Retrieval performance under different number of training data
表 1 土壤湿度反演仿真结果统计
Table 1. Statistical results of simulation of soil moisture retrieval
极化情况 反演模型 均方根误差/
（cm3·cm−3）相关系数 有交叉极化 半经验模型 0.012 3 0.9962 一阶模型 0.083 2 0.8090 二阶模型 0.0793 0.8297 无交叉极化 半经验模型 0.0145 0.9959 一阶模型 0.0346 0.9741 二阶模型 0.0324 0.9775 
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表 2 实验期间GEO卫星高度角变化情况
Table 2. Variation of GEO satellite elevation angle during experiment
(°) 卫星号 最低高度角 最高高度角 1 36.7389 39.5104 2 31.8213 34.3298 3 42.4344 44.8786 4 25.6247 27.0136 5 14.7317 16.9901
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[1] 裴悦琨, 韩心新. GNSS-R探测土壤湿度综述[J]. 大地测量与地球动力学, 2021, 41(2): 140-144. doi: 10.14075/j.jgg.2021.02.006PEI Y K, HAN X X. Overview of GNSS-R technique for soil moisture detection[J]. Journal of Geodesy and Geodynamics, 2021, 41(2): 140-144(in Chinese). doi: 10.14075/j.jgg.2021.02.006 [2] LI F, PENG X F, CHEN X W, et al. Analysis of key issues on GNSS-R soil moisture retrieval based on different antenna patterns[J]. Sensors, 2018, 18(8): 2498. doi: 10.3390/s18082498 [3] EDOKOSSI K, CALABIA A, JIN S G, et al. GNSS-reflectometry and remote sensing of soil moisture: A review of measurement techniques, methods, and applications[J]. Remote Sensing, 2020, 12(4): 614. doi: 10.3390/rs12040614 [4] MARTÍN-NEIRA M. A pasive reflectometry and interferometry system (PARIS) application to ocean altimetry[J]. ESA Journal, 1993, 17(4): 331-355. [5] ZHENG N Q, CHEN P, LI Z. Accuracy analysis of ground-based GNSS-R sea level monitoring based on multi GNSS and multi SNR[J]. Advances in Space Research, 2021, 68(4): 1789-1801. doi: 10.1016/j.asr.2021.04.024 [6] GAO F, XU T H, MENG X Y, et al. A coastal experiment for GNSS-R code-level altimetry using BDS-3 new civil signals[J]. Remote Sensing, 2021, 13(7): 1378. doi: 10.3390/rs13071378 [7] LIU B J, WAN W, HONG Y. Can the accuracy of sea surface salinity measurement be improved by incorporating spaceborne GNSS-reflectometry?[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(1): 3-7. doi: 10.1109/LGRS.2020.2967472 [8] LARSON K M, SMALL E E, GUTMANN E, 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 [9] 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): L24405. doi: 10.1029/2008GL036013 [10] JIA Y, JIN S G, SAVI P, et al. Modeling and theoretical analysis of GNSS-R soil moisture retrieval based on the random forest and support vector machine learning approach[J]. Remote Sensing, 2020, 12(22): 3679. doi: 10.3390/rs12223679 [11] GEREMIA-NIEVINSKI F, HOBIGER T, HAAS R, et al. SNR-based GNSS reflectometry for coastal sea-level altimetry: Results from the first IAG inter-comparison campaign[J]. Journal of Geodesy, 2020, 94(8): 1-15. [12] MARTÍN A, LUJÁN R, ANQUELA A B. Python software tools for GNSS interferometric reflectometry (GNSS-IR)[J]. GPS Solutions, 2020, 24(4): 94. doi: 10.1007/s10291-020-01010-0 [13] 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, 2013, 52(1): 537-543. [14] 张楠, 严颂华, 王文伟. 北斗GEO卫星微动条件下的GNSS-R土壤湿度反演[J]. 科学技术与工程, 2019, 19(9): 154-161.ZHANG N, YAN S H, WANG W W. Soil moisture inversion based on GNSS-R under the micro-motion condition of Beidou GEO satellites[J]. Science Technology and Engineering, 2019, 19(9): 154-161(in Chinese). [15] 邹文博, 张波, 洪学宝, 等. 利用北斗GEO卫星反射信号反演土壤湿度[J]. 测绘学报, 2016, 45(2): 199-204. doi: 10.11947/j.AGCS.2016.20150135ZOU W B, ZHANG B, HONG X B, et al. Soil moisture retrieval using reflected signals of BeiDou GEO satellites[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(2): 199-204(in Chinese). doi: 10.11947/j.AGCS.2016.20150135 [16] 杨磊, 吴秋兰, 张波, 等. SVRM辅助的北斗GEO卫星反射信号土壤湿度反演方法[J]. 北京航空航天大学学报, 2016, 42(6): 1134-1141. doi: 10.13700/j.bh.1001-5965.2015.0656YANG L, WU Q L, ZHANG B, et al. SVRM-assisted soil moisture retrieval method using reflected signal from BeiDou GEO satellites[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1134-1141(in Chinese). doi: 10.13700/j.bh.1001-5965.2015.0656 [17] 班伟. 利用GNSS反射信号反演水位、积雪厚度和土壤湿度的方法研究[D]. 武汉: 武汉大学, 2017.BAN W. Research on retrieving water level, snow depth and soil moisture using GNSS-reflected signal[D]. Wuhan: Wuhan University, 2017 (in Chinese). [18] BAN W, YU K G, ZHANG X H. GEO-satellite-based reflectometry for soil moisture estimation: Signal modeling and algorithm development[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1829-1838. doi: 10.1109/TGRS.2017.2768555 [19] 李培昊. 基于北斗GEO卫星信噪比的土壤湿度反演[D]. 成都: 西南交通大学, 2019.LI P H. Soil moisture inversion based on Beidou GEO satellite signal-to-noise ratio[D]. Chengdu: Southwest Jiaotong University, 2019 (in Chinese). [20] ZAVOROTNY V U, LARSON K M, BRAUN J J, et al. A physical model for GPS multipath caused by land reflections: Toward bare soil moisture retrievals[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1): 100-110. doi: 10.1109/JSTARS.2009.2033608 [21] BALANIS C A. Antenna theory analysis & design[M]. New York: John Wiley, 1996 [22] 万玮, 李黄, 洪阳. 作为外辐射源雷达的GNSS-R遥感多极化问题[J]. 雷达学报, 2014, 3(6): 641-651.WAN W, LI H, HONG Y. Issues on multi-polarization of GNSS-R for passive radar detection[J]. Journal of Radars, 2014, 3(6): 641-651(in Chinese). [23] 刘军, 赵少杰, 蒋玲梅, 等. 微波波段土壤的介电常数模型研究进展[J]. 遥感信息, 2015, 30(1): 5-13. doi: 10.3969/j.issn.1000-3177.2015.01.002LIU J, ZHAO S J, JIANG L M, et al. Research progress on dielectric constant model of soil at microwave frequency[J]. Remote Sensing Information, 2015, 30(1): 5-13(in Chinese). doi: 10.3969/j.issn.1000-3177.2015.01.002 [24] HALLIKAINEN M T, ULABY F T, DOBSON M C, et al. Microwave dielectric behavior of wet soil-part 1: Empirical models and experimental observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 1985, 23(1): 25-34. doi: 10.1109/TGRS.1985.289497 [25] REES W G. Physical principles of remote sensing[M]. Cambridge: Cambridge University Press, 2013. [26] DAVIES H. The reflection of electromagnetic waves from a rough surface[J]. Proceedings of the IEE - Part IV:Institution Monographs, 1954, 101(7): 209-214. doi: 10.1049/pi-4.1954.0025 [27] 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 [28] ZHANG S B, ROUSSEL N, BONIFACE K, et al. Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop[J]. Hydrology and Earth System Sciences, 2017, 21(9): 4767-4784. doi: 10.5194/hess-21-4767-2017 [29] WIGNERON J P, CALVET J C, PELLARIN T, et al. Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans[J]. Remote Sensing of Environment, 2003, 85(4): 489-506. doi: 10.1016/S0034-4257(03)00051-8 [30] AL-KHALDI M M, JOHNSON J T, O’BRIEN A J, et al. Time-series retrieval of soil moisture using CYGNSS[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4322-4331. doi: 10.1109/TGRS.2018.2890646 -
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