Modeling of EM Wave Coherent Scattering From a Rough Multilayered Medium With the Scalar Kirchhoff Approximation for GPR Applications

Pinel, Nicolás; Bastard, Cédric Le; Bourlier, Christophe

doi:10.1109/tgrs.2019.2947356

Cited by 16 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, two-step calibration procedures or empirical and semi-empirical soil moisture (SM) models can be used to accurately determine the soil moisture content (Dominguez-Nino et al, 2019;Jones et al, 2005). The most frequently-used SM models are the following: i) Kirchhoff Approximation Model (KAM) (Pinel et al, 2020); ii) Integral Equation Model (IEM) (Zhang et al, 2020); iii) Small Perturbation Model (SPM) (Burkholder et al, 2017); iv) Small Slope Approximation Method (SSAM) (Zhang et al, 2018), as well as the semi-empirical models such as the v) Dubois model (Zhu et al, 2019) and the iv) Oh model (Sekertekin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

Kulmány

Bede‐Fazekas

Beslin

et al. 2022

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermogravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.

show abstract

Section: Introductionmentioning

confidence: 99%

Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

Kulmány

Bede‐Fazekas

Beslin

et al. 2022

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

show abstract

“…Field measurements are subject to many uncontrolled variables when contrasted with model results. Factors such as soil variability, system cables, imperfect simulation of shields and surface irregularity (Babcock et al., 2016; Giannopoulos & Diamanti, 2008; Pinel et al., 2020; van der Kruk et al., 2012), all contribute to variability in GPR signals from the ideal model. The results we obtained in the field are similar to modelled responses but by no means identical.…”

Section: Resultsmentioning

confidence: 99%

Studying GPR's direct and reflected waves

Tokmaktsi,

Diamanti,

Vargemezis

et al. 2024

Near Surface Geophysics

View full text Add to dashboard Cite

As the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground‐penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common‐offset mode around a transmitting antenna. In the field, we also collected wide‐angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three‐dimensional finite‐difference time‐domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation.

show abstract

“…Various empirical soil moisture models, including the Kirchhoff Approximation Model (KAM) [ 7 ] and the Small Perturbation Model (SPM) [ 8 ], have been used to estimate soil moisture. These models were among the earliest approaches to analyzing scattering from rough surfaces, while more modern approaches like Integral Equation Model (IEM) [ 9 ] and Small Slope Approximation Method (SSAM) [ 10 ] are considered to have a larger validity range for monitoring scattering from rough surfaces.…”

Section: Introductionmentioning

confidence: 99%

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

View full text Add to dashboard Cite

This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

show abstract

Modeling of EM Wave Coherent Scattering From a Rough Multilayered Medium With the Scalar Kirchhoff Approximation for GPR Applications

Cited by 16 publications

References 30 publications

Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture

Studying GPR's direct and reflected waves

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Contact Info

Product

Resources

About