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<p>Accurate soil moisture monitoring is essential for agriculture and disaster prevention. However, existing methods often rely on expensive sensors or are limited by satellite coverage. In this study, we propose a low-cost and practical method for estimating soil permittivity using signal-to-noise ratio (SNR) data in the National Marine Electronics Association (NMEA) format from low-cost mass-market GNSS receivers. We developed a theoretical model that links SNR amplitude to soil permittivity via the Fresnel reflection coefficient and validated it through field experiments at multiple antenna heights. By interpolating the elevation angle data and analyzing the SNR amplitude at elevation angles of 20°or greater, we minimized the effects of nonlinearity and achieved a strong correlation with independently measured soil permittivity. Our results demonstrate that lower antenna heights enhance accuracy and reduce variability. This method enables calibration at a single location and permits accurate permittivity estimation at other sites using only low-cost mass-market GNSS devices. The approach offers a scalable and affordable solution for soil moisture estimation across various soil textures, thereby enhancing the applicability of GNSS-IR in precision agriculture and environmental monitoring.</p>

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Soil permittivity measurement using interferometry method with mass-market grade GNSS devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

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  • Daiki Kobayashi,
  • Shunsuke Kodaira,
  • Yuichi Maruo,
  • Shinsuke Aoki,
  • Kosuke Noborio

摘要

Accurate soil moisture monitoring is essential for agriculture and disaster prevention. However, existing methods often rely on expensive sensors or are limited by satellite coverage. In this study, we propose a low-cost and practical method for estimating soil permittivity using signal-to-noise ratio (SNR) data in the National Marine Electronics Association (NMEA) format from low-cost mass-market GNSS receivers. We developed a theoretical model that links SNR amplitude to soil permittivity via the Fresnel reflection coefficient and validated it through field experiments at multiple antenna heights. By interpolating the elevation angle data and analyzing the SNR amplitude at elevation angles of 20°or greater, we minimized the effects of nonlinearity and achieved a strong correlation with independently measured soil permittivity. Our results demonstrate that lower antenna heights enhance accuracy and reduce variability. This method enables calibration at a single location and permits accurate permittivity estimation at other sites using only low-cost mass-market GNSS devices. The approach offers a scalable and affordable solution for soil moisture estimation across various soil textures, thereby enhancing the applicability of GNSS-IR in precision agriculture and environmental monitoring.