The calculation of reflections of linear polarization plane electromagnetic wave from the boundary of the «air – wet soil» based on heterogeneous Maxwell Garnett and Brughehman models


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Abstract

In this work we calculated the reflection coefficients modules of a linear polarization plane electromagnetic wave depending on soil moisture with a relative complex dielectric constant described by the heterogeneous Maxwell Garnett and Bruggeman models. A comparison is made of the calculated dependences of reflection coefficients for incident E- and H-polarization waves on soil moisture according to the two proposed heterogeneous two-component models. The correctness of the applied models is shown within the soil moisture content up to 10 %. Within the limits of soil moisture change from 10 to 50 %, there are slight discrepancies in the calculation results for two heterogeneous models. The reflection level of an electromagnetic wave from the soil surface in the case of its H-polarization is less than in the case of E-polarization. With an increase in soil moisture, a monotonous increase in the reflection level is observed. The proposed heterogeneous models of wet soil and the calculation method can be used for remote radar sensing of the Earth’s surface in order to determine the moisture content in the rooted layer of the soil.

About the authors

Dmitry N. Panin

Povolzhskiy State University of Telecommunications and Informatics

Author for correspondence.
Email: panin-dn@psuti.ru
23, L. Tolstoy Street, Samara, 443010, Russia

Oleg V. Osipov

Povolzhskiy State University of Telecommunications and Informatics

Email: o.osipov@psuti.ru
23, L. Tolstoy Street, Samara, 443010, Russia

Kirill O. Bezlyudnikov

Povolzhskiy State University of Telecommunications and Informatics

Email: yakobix@ya.ru
23, L. Tolstoy Street, Samara, 443010, Russia

References

  1. Martínez-Fernández J., González-Zamora A., Almendra-Martín L. Soil moisture memory and soil properties: An analysis with the stored precipitation fraction // Journal of Hydrology. 2021. Vol. 593. P. 125622. DOI: https://doi.org/10.1016/j.jhydrol.2020.125622
  2. Borodychev V.V., Lytov M.N. Irrigation management model based on soil moisture distribution profile // IOP Conference Series: Earth and Environmental Science. 2020. Vol. 577, no. 1. P. 012022. DOI: https://doi.org/10.1088/1755-1315/577/1/012022
  3. Shutko A.M., Reutov E.A., Golovachev S.P. Estimation of soil moisture profiles and root zone moisture content by means of microwave radiometry and a priori information // Passive Microwave Remote Sensing of Land-Atmosphere Interactions. Berlin: De Gruyter, 2020. P. 461–474. DOI: https://doi.org/10.1515/9783112319307-toc
  4. Hao X., Hao H., Zhang J. Soil moisture influenced the variability of air temperature and oasis effect in a large inland basin of an arid region // Hydrological Processes. 2021. Vol. 35, no. 6. P. 14246. DOI: https://doi.org/10.1002/hyp.14246
  5. Bo T., Baowen Y. Effect of cavity structure on the saving up and dissipation of moisture in loess soil // IOP Conference Series: Earth and Environmental Science. 2021. Vol. 791, no. 1. P. 012013. DOI: https://doi.org/10.1088/1755-1315/791/1/012013
  6. Sungmin O., Orth R. Global soil moisture data derived through machine learning trained with in-situ measurements // Scientific Data. 2021. Vol. 8, no. 1. P. 1–14. DOI: https://doi.org/10.6084/m9.figshare.14790510
  7. Grillakis M.G. Increase in severe and extreme soil moisture droughts for Europe under climate change // Science of the Total Environment. 2019. Vol. 660. P. 1245–1255. DOI: https://doi.org/10.1016/j.scitotenv.2019.01.001
  8. Berg A., Sheffield J. Climate change and drought: the soil moisture perspective // Current Climate Change Reports. 2018. Vol. 4, no. 2. P. 180–191. DOI: https://doi.org/10.1007/s40641-018-0095-0
  9. Srivastava P.K. Satellite soil moisture: Review of theory and applications in water resources // Water Resources Management. 2017. Vol. 31, no. 10. P. 3161–3176. DOI: https://doi.org/10.1007/s11269-017-1722-6
  10. Kim H., Lakshmi V. Use of Cyclone Global Navigation Satellite System (CYGNSS) observations for estimation of soil moisture // Geophysical Research Letters. 2018. Vol. 45, no. 16. P. 8272–8282. DOI: https://doi.org/10.1029/2018GL078923
  11. Chew C., Small E. Description of the UCAR/CU soil moisture product // Remote Sensing. 2020. Vol. 12, no. 10. P. 1558. DOI: https://doi.org/10.3390/rs12101558
  12. Fang K., Shen C. Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel // Journal of Hydrometeorology. 2020. Vol. 21, no. 3. P. 399–413. DOI: https://doi.org/10.1175/JHM-D-19-0169.1
  13. Матвеев И.В., Осипов О.В., Панин Д.Н. Математическая модель неоднородной комплексной диэлектрической проницаемости влажной почвы с учетом гетерогенности // Взаимодействие сверхвысокочастотного, терагерцового и оптического излучения с полупроводниковыми микро- и наноструктурами, метаматериалами и биообъектами: сб. статей восьмой Всероссийской научной школы-семинара. 2021. С. 237–241.
  14. Матвеев И.В., Осипов О.В., Панин Д.Н. Взаимодействие электромагнитной волны с киральным метаматериалом на основе модели Максвелла Гарнетта // IV Научный форум телекоммуникации: теория и технологии (ТТТ-2020). Физика и технические приложения волновых процессов (ФиТПВП-2020). 2020. С. 220–221.
  15. Рекомендация МСЭ-R P.527-4 от 06/2017. Электрические характеристики земной поверхности. Серия Р. Распространение радиоволн.

Copyright (c) 2022 Panin D.N., Osipov O.V., Bezlyudnikov K.O.

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