留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

利用北斗GEO卫星干涉信号功率反演土壤湿度

汉牟田 许志超 常青 张波 王峰 洪学宝

汉牟田,许志超,常青,等. 利用北斗GEO卫星干涉信号功率反演土壤湿度[J]. 北京航空航天大学学报,2023,49(7):1661-1670 doi: 10.13700/j.bh.1001-5965.2021.0478
引用本文: 汉牟田,许志超,常青,等. 利用北斗GEO卫星干涉信号功率反演土壤湿度[J]. 北京航空航天大学学报,2023,49(7):1661-1670 doi: 10.13700/j.bh.1001-5965.2021.0478
HAN M T,XU Z C,CHANG Q,et al. Soil moisture retrieval using Beidou GEO satellite interference signal power[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1661-1670 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0478
Citation: HAN M T,XU Z C,CHANG Q,et al. Soil moisture retrieval using Beidou GEO satellite interference signal power[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1661-1670 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0478

利用北斗GEO卫星干涉信号功率反演土壤湿度

doi: 10.13700/j.bh.1001-5965.2021.0478
基金项目: 国家自然科学基金(41774028); 博士后创新人才支持计划(BX20200039)
详细信息
    通讯作者:

    E-mail:by1602112@buaa.edu.cn

  • 中图分类号: V19;TP79

Soil moisture retrieval using Beidou GEO satellite interference signal power

Funds: National Natural Science Foundation of China (41774028); Post Doctoral Innovative Talent Support Program (BX20200039)
More Information
  • 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。

     

  • 图 1  干涉测量场景

    Figure 1.  Scenario of interferometric measurement.

    图 2  RHCP与LHCP分量反射系数模值

    Figure 2.  Modulus of RHCP and LHCP component reflection coefficient.

    图 3  总体反演流程

    Figure 3.  Overall retrieval workflow

    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 6  反射系数反演结果

    Figure 6.  Retrieval results of the reflection coefficient

    图 7  土壤湿度反演结果

    Figure 7.  Retrieval results of soil moisture

    图 8  实验地点卫星地图

    Figure 8.  Satellite map of experiment site

    图 9  实验期间土壤湿度变化

    Figure 9.  Soil moisture variation during experiment

    图 10  4号星与5号星载噪比变化

    Figure 10.  C/N0 variation of satellite No.4 and No.5.

    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 12  半经验模型训练结果

    Figure 12.  Training results of semi-empirical model

    图 13  土壤湿度反演结果

    Figure 13.  Soil moisture retrieval results

    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

    图 15  土壤湿度反演误差仿真

    Figure 15.  Simulation of soil moisture retrieval error

    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 30.9962
    一阶模型0.083 20.8090
    二阶模型0.07930.8297
    无交叉极化半经验模型0.01450.9959
    一阶模型0.03460.9741
    二阶模型0.03240.9775
    下载: 导出CSV

    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
    136.738939.5104
    231.821334.3298
    342.434444.8786
    425.624727.0136
    514.731716.9901
    下载: 导出CSV
  • [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.2021.02.006

    PEI 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.2016.20150135

    ZOU 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.1001-5965.2015.0656

    YANG 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.1000-3177.2015.01.002

    LIU 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
  • 加载中
图(15) / 表(2)
计量
  • 文章访问数:  194
  • HTML全文浏览量:  45
  • PDF下载量:  19
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-20
  • 录用日期:  2021-11-20
  • 网络出版日期:  2021-11-30
  • 整期出版日期:  2023-07-31

目录

    /

    返回文章
    返回
    常见问答