The suitability analysis of soil moisture retrieval using GNSS-R technology

  • role: First author第一作者
  • Affiliation:

    Institute of Remote Sensing & Geographical Information System, Peking University, Beijing 100871, China

  • Email:pengxf@most.cn
  • Introduction:彭学峰(1989— ),男,硕士研究生,研究方向为GNSS-R理论及土壤水分遥感应用。E-mail:pengxf@most.cn
PENG Xuefeng1,  
  • role: Corresponding author通信作者
  • Affiliation:

    Institute of Remote Sensing & Geographical Information System, Peking University, Beijing 100871, China

  • Email:wanweilizzy@tsinghua.edu.cn
  • Introduction:万玮(1986— ),女,助理研究员,研究方向为GNSS-R遥感理论、方法及应用,水循环遥感等。E-mail:wanweilizzy@tsinghua.edu.cn
WAN Wei*,  
  • Affiliation:

    Institute of Remote Sensing & Geographical Information System, Peking University, Beijing 100871, China

LI Fei1,  
  • Affiliation:

    Institute of Remote Sensing & Geographical Information System, Peking University, Beijing 100871, China

CHEN Xiuwan1

Resümee

Soil moisture measurements are important to hydrology, climatology, and agriculture. Global Navigation Satellite System reflectometry (GNSS-R) is a new and powerful tool for sensing soil moisture. Different methods have been proposed for the reception and processing of reflected signals, such as multiantenna and single-antenna patterns. Considerable efforts have been made on ground-based and airborne observations. Meanwhile, a number of space-borne missions have also been implemented. During the suitability analysis of soil moisture remote sensing via GNSS-R technique, three key factors have to be determined—the specular reflection point, spatial pixel size, and sensing depth in the soil—regardless of the extraction methods of the reflected signals or the types of the observation platform. However, no relatively comprehensive explanations are available in the current literature. Theoretical analysis and formula derivation are conducted to systematically and quantitatively determine the extent of soil detection in three dimensions from the abovementioned aspects. First, the geographical position of the specular reflection point on the WGS84 ellipsoid surface is determined with an iterative algorithm. The effect of elevation between the real ground surface and the ellipsoid is discussed. Second, the first Fresnel zone is defined as the spatial pixel of the GNSS-R technique. The size of the pixel is regarded as the spatial resolution based on the concept of the equi-delay ellipse. Thirdly, the penetration distance of GNSS signals in soil is expressed. The concept of sensing depth is proposed on penetration distance and is based on the theory of microwave remote sensing. This concept makes soil moisture detection more describable and comparable. To further analyze the feasibility of soil moisture remote sensing with GNSS-R, the results of two application scenarios are shown: (1) A ground-based GPS measurement was performed in Marshall, Colorado, US, from the Plate Boundary Observatory. This measurement corresponds to the single-antenna pattern. The relative location of the specular reflection points, average area of the first Fresnel ellipse clusters, and sensing depth of the time-series soil moisture are analyzed. (2)An airborne GNSS-R experiment was conducted in Zhengzhou to obtain soil moisture content. This measurement corresponds to the multiantenna pattern. The spatial distribution of estimated soil moisture with a certain resolution based on flight tracks and relevant sensing depth are manifested. The results of the ground-based GPS measurement and airborne GNSS-R experiment are reasonable and convincing. Given that this study determined the extent of detected soil in three dimensions, this work provides theoretical basis for the precision evaluation of retrieved soil moisture. BeiDou mainly differs from GPS in the carrier frequency for remote sensing using GNSS reflected signals. Therefore, the results of this study provide references for future development of the BeiDou-R technique in China.

Schlüsselwort

GNSS-R;soil moisture;specular reflection point;resolution;sensing depth

References

  1. 1.
    Alonso-Arroyo A, Forte G, Camps A, Park H, Pascual D, Onrubia R and Jove-Casulleras R. 2013. Soil Moisture mapping using forward scattered GPS L1 signals // Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne, VIC: IEEE: 354–357
  2. 2.
    Alonso-Arroyo A, Camps A, Monerris A, Rüdiger C, Walker J P, Forte G, Pascual D, Park H and Onrubia R. 2014. The light airborne reflectometer for GNSS-R observations (LARGO) instrument: initial results from airborne and Rover field campaigns // Proceedings of 2014 IEEE International Geoscience and Remote Sensing Symposium. Quebec City, QC: IEEE: 4054–4057
  3. 3.
    Alonso-Arroyo A, Torrecilla S, Querol J, Camps A, Pascual D, Park H and Onrubia R. 2015. Two dedicated soil moisture experiments using the scatterometric properties of GNSS-reflectometry // Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan: IEEE: 3921–3924
  4. 4.
    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
  5. 5.
    Chew C, Shah R, Zuffada C, Hajj G, Masters D and Mannucci A J. 2016. Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission. Geophysical Research Letters, 43(7): 3317–3324
  6. 6.
    Egido A, Caparrini M, Ruffini G, Paloscia S, Santi E, Guerriero L, Pierdicca N and Floury N. 2012. Global navigation satellite systems reflectometry as a remote sensing tool for agriculture. Remote Sensing, 4(8): 2356–2372
  7. 7.
    Egido A, Paloscia S, Motte E, Guerriero L, Pierdicca N, Caparrini M, Santi E, Fontanelli G and Floury N. 2014. Airborne GNSS-R polarimetric measurements for soil moisture and above-ground biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5): 1522–1532
  8. 8.
    Garrison J L, Cardellach E, Gleason S and Katzberg S. 2014. Foreword to special issue on reflectometry using global navigation satellite systems and other signals of opportunity (GNSS+R). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5): 1412–1415
  9. 9.
    Gleason S. 2006. Detecting bistatically reflected GPS signals from low earth orbit over land surfaces // Proceedings of 2006 IEEE International Symposium on Geoscience and Remote Sensing. Denver, CO, USA: IEEE: 3069–3072 [DOI: 10.1109/IGARSS.2006.792]
  10. 10.
    Guan Z, Zhao K and Song D S. 2006. Measuring soil moisture using reflected GPS signals. Advances in Earth Science, 21(7): 747–750
  11. 11.
    Hallikainen M T, Ulaby F T, Dobson M C, EL-Rayes M A and Wu L K. 1985. Microwave dielectric behavior of wet soil-part 1: empirical models and experimental observations. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1): 25–34
  12. 12.
    Jales P and Unwin M. 2015. Mission Description-GNSS Reflectometry on TDS-1 with the SGR-ReSI. Guildford, UK: Surrey Satellite Technology Ltd
  13. 13.
    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
  14. 14.
    Jin S G and Komjathy A. 2010. GNSS reflectometry and remote sensing: new objectives and results. Advances in Space Research, 46(2): 111–117
  15. 15.
    Kaplan E and Hegarty C. 2005. Understanding GPS: Principles and Applications. 2nd ed. Norwood: Artech House: 32–34
  16. 16.
    Katzberg S J and Garrison J L. 1996. Utilizing GPS to Determine Ionospheric Delay over the Ocean. [s.l.]: National Aeronautics and Space Administration Langley Research Center: 1–16
  17. 17.
    Katzberg S J, Torres O, Grant M S and Masters D. 2006. Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: results from SMEX02. Remote Sensing of Environment, 100(1): 17–28
  18. 18.
    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
  19. 19.
    Li H, Xia Q, Yin C and Wan W. 2013. The current status of research on GNSS-R remote sensing technology in China and future development. Journal of Radars, 2(4): 389–399
  20. 20.
    Liu J N, Shao L J and Zhang X X. 2007. Advances in GNSS-R studies and key technologies. Geomatics and Information Science of Wuhan University, 32(11): 955–960
  21. 21.
    Mao K B, Wang J M, Zhang M Y, Tang H J and Zhou Q B. 2009. The study of soil moisture retrieval from GNSS-R signals based on AIEM model and experiment data. Chinese High Technology Letters, 19(3): 295–301
  22. 22.
    Martín-Neira M, D’Addio S, Buck C, Floury N and Prieto-Cerdeira R. 2011. The PARIS ocean altimeter in-orbit demonstrator. IEEE Transactions on Geoscience and Remote Sensing, 49(6): 2209–2237
  23. 23.
    Masters D, Axelrad P and Katzberg S. 2004. Initial results of land-reflected GPS bistatic radar measurements in SMEX02. Remote Sensing of Environment, 92(4): 507–520
  24. 24.
    Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Vall-Llossera M, Valencia E, Marchan-Hernandez J F and Ramos-Perez I. 2009. Soil moisture retrieval using GNSS-R techniques: experimental results over a bare soil field. IEEE Transactions on Geoscience and Remote Sensing, 47(11): 3616–3624
  25. 25.
    Song X Z, Xu A G, Yang D K and Ma X D. 2013. Details of soil moisture measuring utilizing GNSS reflected signals. Bulletin of Surveying and Mapping, (11): 61–64
  26. 26.
    Torres O. 2004. Analysis of Reflected Global Positioning System Signals as a Method for the Determination of Soil Moisture. El Paso: University of Texas at El Paso: 6–33
  27. 27.
    Troglia Gamba M, Marucco G, Pini M, Ugazio S, Falletti E and Lo Presti L. 2015. Prototyping a GNSS-based passive radar for uavs: an instrument to classify the water content feature of lands. Sensors, 15(11): 28287–28313
  28. 28.
    Ulaby F T, Moore R K and Fung A K. 1981. Microwave Remote Sensing: Active and Passive, Volume 1: Microwave Remote Sensing Fundamentals and Radiometry. Norwood: Artech House: 40–60
  29. 29.
    Ulaby F T, Moore R K and Fung A K. 1986a. Microwave Remote Sensing: Active and Passive, Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory. Norwood: Artech House: 844–847
  30. 30.
    Ulaby F T, Moore R K and Fung A K. 1986b. Microwave Remote Sensing: Active and Passive Volume III: From Theory to Applications. Norwood: Artech House: 1523–1524
  31. 31.
    Wan W, Bai W H, Zhao L M, Long D, Sun Y Q, Meng X G, Chen H, Cui X and Hong Y. 2015. Initial results of China’s GNSS-R airborne campaign: soil moisture retrievals. Science Bulletin, 60(10): 964–971
  32. 32.
    Wan W, Li H, Hong Y, Chen X W and Peng X F. 2015. Toward the patterns of GNSS-R observations: definition and application. Journal of Remote Sensing, 19(6): 882–893
  33. 33.
    Wickert J, Andersen O B, Beyerle G, Chapron B, Cardellach E, D’Addio S, Foerste C, Gommenginger C, Gruber T, Helm A, Hess M, Hoeg P, Jaeggi A, Jakowski N, Kern M, Lee T, Martin-Neira M, Montenbruck O, Pierdicca N, Rius A, Rothacher M, Shum C and Zuffada C. 2013. GEROS-ISS: innovative GNSS reflectometry/ occultation payload onboard the International Space Station for the Global Geodetic Observing System //Proceedings of 2013 AGU Fall Meeting Abstracts. [s.l.]: AGU: 0871
  34. 34.
    Yan S H, Gong J Y, Zhang X X and Li D X. 2011. Ground based GNSS-R observations for soil moisture. Chinese Journal of Geophysics, 54(11): 2735–2744
  35. 35.
    Yang L, Wu Q L, Zhang B, Liang Y, Hong X B and Zou W B. 2016. SVRM-assisted soil moisture retrieval method using reflected signal from BeiDou GEO satellites. Journal of Beijing University of Aeronautics and Astronautics, 42(6): 1134–1141
  36. 36.
    Zhang X J, Zhang W J, Lu X R, Kang G J and Fang G Y. 2007. Dual-frequency synthetic aperture radar for deep soil moisture estimation. Journal of Electronics & Information Technology, 29(11): 2711–2714

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website