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[in Japanese]
2025Volume 159 Pages
1-2
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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Thermoelectric cooler-based net radiometer and capacitor-resistor oscillation-based soil moisture sensor
Kosuke NOBORIO, Jun-ichi TAZAWA, Yuki SHOJI, Naoto SHIMOOZONO, Daiki K ...
2025Volume 159 Pages
3-12
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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Using low-cost sensors, the Internet of Things (IoT) on a farm has been popular for monitoring environmental conditions such as temperature, humidity, and CO2 concentration to evaluate mass and energy balances in the field. However, net radiation and soil moisture sensors are still relatively expensive; thus, low-cost sensors have been required to spread IoT technologies on the farm. In recent years, logic ICs and thermoelectric coolers (TECs) have become readily available at very reasonable prices. Here, we introduced low-cost net radiation and soil moisture sensors. First, a TEC was used to measure net radiation after calibration with a linear regression line. The measurement error was approximately < ±50 W m−2 after eliminating data with the air temperature < the dew point temperature. Then, the capacitor and resistor (CR) oscillator circuit was employed to determine soil water content, assuming soil water as a variable capacitor. After insulating sensing rods, the CR oscillator’s output frequency, f , was inversely related to volumetric water contentment, θ . A linear regression line between θ and f yielded a measurement error of < ±0.05 m3 m−3 with minimal temperature dependence of dθ /dT = 0.0006 m3 m−3 ◦C−1 for 10–40 ◦C. The data obtained using those sensors would be accurate enough for practical use with IoT.
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Shun KIKUCHI, Hirotaka SAITO, Masato OISHI
2025Volume 159 Pages
13-24
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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In artificial ground freezing methods, frozen soil is artificially created to ensure the strength and impermeability of the soil. It is crucial to predict the freezing process of soil by considering the latent heat release during the freezing process. In this study, a computational method for handling latent heat in the freezing processes of saturated ground is investigated by comparing analytical results. The finite element method with stabilization technique was used to formulate the heat conduction equation, and the latent heat was evaluated using the temperature recovery method, the Generalized Clausius-Clapeyron (GCC) equation, and the exponential function model. The results showed that while any of the latent heat treatment methods can be used to analyze the latent heat under conditions where freezing does not take a long time, the use of either the GCC or exponential function model methods is more reliable than the temperature recovery method under conditions where freezing takes a long time.
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Daiki KOBAYASHI, Sona TOKUYAMA, Kosuke NOBORIO
2025Volume 159 Pages
27-32
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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Time-domain reflectometry (TDR) is a widely used method for accurately measuring soil moisture in the field. This technique determines the relative permittivity of the soil by analyzing the change in the propagation speed of electromagnetic waves through a soil probe. However, conventional TDR equipment is expensive, often costing approximately one million yen, which limits its accessibility. Affordable vector network analyzers (VNAs) such as
NanoVNA have emerged as potential alternatives for TDR measurements. Studies have demonstrated that VNAs can reproduce TDR waveforms with comparable accuracy to conventional equipment by converting frequency-domain reflectometry (FDR) data into time-domain waveforms. For example, NanoVNA was introduced recently to measure soil permittivity using three-wire probes accurately. However, this procedure often requires knowledge of digital signal processing; furthermore, the detailed steps for reproducing the results have not yet to be fully documented, creating challenges for widespread adoption. This study aimed to explain the theoretical principles and experimental procedures for reproducing TDR waveforms using NanoVNA. The discussion includes the basics of FDR-to-TDRconversion using the inverse fast Fourier transform (IFFT) and practical considerations, such as calibration, probe configuration, and data processing. In addition, examples of TDR waveformreproduction are reviewed, illustrating the accuracy of air and water permittivity measured. To promote broader accessibility, we provide open-source Python code that enables users to convert NanoVNA data into TDR waveforms. This article may support researchers and engineers in adopting NanoVNAbased TDR measurements as a low-cost yet reliable alternative to conventional TDR systems.
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Koharu TASAKI, Nobuo TORIDE, Shohei KOIZUMI, Ieyasu TOKUMOTO
2025Volume 159 Pages
33-57
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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We developed a reactive transport model that describes aerobic and anaerobic soil organic matter (SOM) decomposition, carbon and nitrogen cycling, ion exchange, and mineral dissolution and precipitation in paddy soils
using the HYDRUS1D-PHREEQC (HP1) program. The first-order organic matter decomposition model based on
LEACHM was modified to incorporate oxidation of organic carbon, sequential reduction of electron acceptors,
and pH buffering of variable charges. These modifications enable the model to simulate aerobic and anaerobic SOM decomposition processes depending on redox potential (Eh) and pH. A numerical experiment was conducted, assuming a one-dimensional soil layer with organic matter application under saturated water flow conditions. The results successfully replicated the formation of about 1 cm surface oxidized layer and a reduced layer where reduction has progressed to the methane generation, and evaluated the concentration distribution changes of each component, as well as reactive transport, including ion exchange, depending on Eh and pH. Furthermore, the dissolution and precipitation of minerals such as manganite and goethite had significant impacts on the composition of the soil solution and solid-phase minerals. The high concentration of FeS in the reduced layer, formed through precipitation, played a critical role in mitigating H2S formation, while also influencing the increase in H2S due to a depletion in Fe2+ concentration.
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Koki OIKAWA, Hirotaka SAITO
2025Volume 159 Pages
59-67
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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Conventional deep learning models do not always follow physical conditions such as governing equations in their predictions. Physics-Informed Neural Networks (PINNs) add physical loss terms to the loss function to evaluate whether the predictions are satisfied with the governing equations and initial and boundary conditions, in addition to a prediction loss term to evaluate the error between the predictions and the training data when learning deep learning models. Conventional numerical inverse analysis to estimate soil hydraulic properties requires appropriate
settings for boundary and initial conditions. One novel example that allows more flexible condition setting than process-based models is an inverse analysis method using PINNs. PINNs, given training data on pressure head and/or volumetric water content and the Richards equation as a governing equation, can predict changes in pressure head over time and the soil hydraulic property profiles. This paper describes examples of PINNs used both in forward and inverse analysis of variably saturated water flow in soils. Finally, we summarize some recent studies using PINNs in the field of soil physics and future issues.
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Yusuke HOMMA, Seiichiro KURODA, Nobuo MAKINO
2025Volume 159 Pages
69-75
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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In this paper, we introduce the application of machine learning and deep learning in the field of soil physics. We then propose using generative AI for saturated and unsaturated flow analysis for artificial embankments such as fill dams and dikes. The generative AI method employed is a generative adversarial network (GAN) model incorporating a U-net. The relationship between hydraulic conductivity distribution and pressure head distribution in a steady state inside the dike is considered as an image-to-image translation problem. For the forward problem of estimating the distribution of pressure head, the accuracy was close to that of the conventional numerical method. On the other hand, for the inverse problem of estimating the distribution of hydraulic conductivity, the estimation accuracy was low. The estimation method using generative AI for the interior of the dike can serve as a means of achieving a digital twin of the dike because the data can be analyzed immediately. This approach is expected to be useful for real time evaluation and anomaly detection in dikes.
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Hideki MIYAMOTO
2025Volume 159 Pages
77-82
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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[in Japanese], [in Japanese]
2025Volume 159 Pages
83-86
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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2025Volume 159 Pages
87-98
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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2025Volume 159 Pages
99-101
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
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[in Japanese]
2025Volume 159 Pages
103-104
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
JOURNAL
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[in Japanese]
2025Volume 159 Pages
105-107
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
JOURNAL
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[in Japanese]
2025Volume 159 Pages
109-110
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
JOURNAL
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[in Japanese]
2025Volume 159 Pages
113
Published: March 20, 2025
Released on J-STAGE: April 21, 2025
JOURNAL
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