Posters on site: Mon, 28 Apr, 16:15–18:00 | Hall X3
The soil-plant-atmosphere continuum (SPAC) is the interconnected water pathway between soil, plants, and atmosphere, and plays a pivotal role in distribution of water and nutrients in terrestrial ecosystems. In order to understand and predict the dynamics between its components, especially in the context of advancing climate change, it is essential to investigate both the above- and below-ground part of the SPAC with high temporal resolution. However, while methods to observe the above-ground part of the plant are frequently employed, due to its inaccessibility, in-situ measurements of root system activity are still scarce.
In this study, we employed a novel combination of sensors at the plot scale to obtain a more complete picture of the dynamics between root water uptake, plant photosynthesis and transpiration, and atmospheric conditions. During the growth season of 2023, we studied the rhizosphere beneath maize plots using spectral electrical impedance tomography, a method which has been shown to be sensitive to soil water content dynamics and root structure and activity. Water transport through the plant stem was monitored via sap flow sensors, while photosynthetic activity and atmospheric conditions were measured continuously using a sun-induced fluorescence sensor and a weather station, respectively. Time series data were analyzed across multiple time windows, focusing on environmental events such as precipitation, prolonged dry periods, and variations in cloud cover.
Our results demonstrate we achieved consistently high-quality electrical impedance data throughout the monitoring period. The electrical imaging results exhibit spatially and temporally well resolved diurnal variations in the subsurface polarization behaviour, suggesting a sensitivity to root ion uptake processes. In particular, variability in polarization signatures was more pronounced near the surface early in the season, and shifted to deeper layers later in the season. We attribute this behaviour to the seasonal shift in water availability towards deeper layers, causing a deeper active root water uptake zone. Additionally, rain events promote polarization variability in shallow soil layers. Above-ground data showed cyclical variations both for sap flow and fluorescence measurements and revealed a clear connection to meteorological conditions such as cloud cover or precipitation, confirming the coupling of above-ground plant activity to the atmosphere. Together, the below- and above-ground observations provide a holistic view of the processes within the SPAC, and allow analysis of the complex relations between transpiration, photosynthesis, and root water uptake. To conclude, this study contributes to a deeper understanding of water uptake and plant activity dynamics in crop systems and may inform the breeding of adapted plant varieties, the optimization of agricultural management practices, and the calibration of physiological models describing the SPAC.
How to cite: Michels, V., De Cannière, S., Lopez, G., Weigand, M., Warstat, K., Siegmann, B., Seidel, S., Muller, O., Rascher, U., Vereecken, H., and Kemna, A.: From root to leaf: Multi-sensor monitoring of the soil-plant-atmosphere continuum, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8723, https://doi.org/10.5194/egusphere-egu25-8723, 2025.
The soil-plant continuum of agricultural crops is regulating key processes that affect plant performance and agricultural productivity. As climate change impacts agricultural systems, understanding these processes will become increasingly important, especially when increasing yield productivity, while minimizing the environmental footprint are key aspects. Quantifying the impact of climate change and management practices on crop growth requires understanding about the dynamics of the root systems of crops. Ground penetrating radar (GPR) combined with root imaging and modeling techniques offers a unique opportunity to study these dynamics in function of soil, climate and management. As a first step, this study examined the relationship between root development and soil dielectric permittivity variability using root images and 200 MHz time-lapse horizontal crosshole GPR at two field minirhizotron (MR) facilities in Selhausen, Germany. The data was acquired over three maize growing seasons, in 7-m long rhizotubes at six different depths, ranging between 0.1 m - 1.2 m and for three different plots with varying agricultural treatments. We calculated trend-corrected spatial permittivity deviations to isolate root-related effects by removing static and dynamic influences caused by soil heterogeneity and changing weather conditions. This permittivity deviation increased during the growing season, correlating with root presence. Cross-correlation analysis between permittivity variability and root volume fraction yielded in coefficients of determination above 0.5 for half of the data pairs. From this study some questions remained unanswered, such as identifying individual roots or quantifying the influence of roots and above-ground shoot on the GPR signal. Subsequently, synthetic forward modeling was conducted using the data acquisition of the previous study as a template and the open-source electromagnetic simulation software gprMax. GPR traces were modeled and analyzed for scenarios with varying soil-plant continuum compositions, including soil, roots, and above-ground shoots in two- or three dimensions. The models incorporated realistic root contributions based on trench wall counts. We found that the presence of roots, which resulted in a permittivity increase on one hand, had a higher influence on the GPR signal than the above-ground shoot and on the other hand the roots affected the first arrival time and amplitudes of the GPR signal. Hence more sophisticated analysis techniques such as full-waveform inversion are necessary. Furthermore, we introduced an approach to derive the soil water content within the soil-plant continuum, where the CRIM petrophysical model was extended with the root phase. This showed that neglecting the root phase leads to overestimation of soil water contents.
How to cite: Lärm, L., Bauer, F., Rödder, J., Vereecken, H., Vanderborght, J., van der Kruk, J., Schnepf, A., and Klotzsche, A.: Investigating the soil-plant continuum of maize crops using ground penetrating radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15134, https://doi.org/10.5194/egusphere-egu25-15134, 2025.
Aligned with Saudi Arabia’s Vision 2030 Green Initiative, this study presents an innovative approach to sustainable agriculture in hyper-arid regions by integrating advanced geophysical methods to monitor tree root water uptake (RWU). The research highlights the combined use of modeling through HYDRUS-1D and Electrical Resistivity Imaging (ERI) for non-invasive root zone monitoring under controlled experimental conditions. The findings address critical challenges in agricultural water management in arid environments, where extreme temperatures and sandy soils significantly impact water dynamics and crop sustainability. RWU patterns of a citrus tree were simulated using HYDRUS-1D under varying soil and climatic conditions. The results revealed that the highest RWU rates occurred in the upper 30 cm of soil, predominantly during the morning. As temperatures increased, RWU activity shifted more profoundly into the soil profile. These insights are crucial for optimizing precision irrigation strategies in water-scarce regions. The model calibration utilized real-time soil moisture data collected through innovative 3D and 4D ERI methods—a seven-month experiment conducted in a controlled outdoor environment in Dhahran, Saudi Arabia. The experimental setup included a 2m x 2m x 2m wooden tank filled with sandy soil, in which a lemon tree was planted and monitored using ERI techniques. The 3D and 4D geoelectrical models captured temporal and spatial variations in root zone moisture content during irrigation events, providing unprecedented insights into subsurface water distribution and root activity dynamics.
A key outcome of the research was the successful detection of root activity through resistivity anomalies, confirming the potential of ERI as a non-invasive tool for root zone monitoring. This novel approach to root zone monitoring offers significant advantages over traditional methods. Unlike invasive techniques, such as soil coring, ERI provides high-resolution data without disrupting the natural state of the root system. Additionally, the continuous monitoring capability of ERI enables dynamic observation of root water uptake patterns over time, supporting the development of more efficient irrigation and water management practices. Integrating geophysical methods with numerical modeling presents a scalable and sustainable solution for addressing water management challenges in agriculture. This research improves water use efficiency, reduces environmental impact, and enhances crop productivity in hyper-arid regions by providing actionable insights into root zone moisture dynamics. The findings have broad applications in precision agriculture and environmental management. They underscore the importance of adopting innovative, non-invasive technologies to optimize resource utilization and achieve sustainable development goals in water-scarce regions.
How to cite: Kirmizakis, P., Pradipta, A., Kourgialas, N., Papadopoulos, N., and Soupios, P.: Towards Green Initiatives: Advancing Root Zone Monitoring Using Non-Invasive Geophysical Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2335, https://doi.org/10.5194/egusphere-egu25-2335, 2025.
Traditional methods for soil moisture prediction often face challenges in providing comprehensive spatial and temporal assessments of root zone soil moisture (RZSM) in complex soil environments. This study proposes a novel approach based on the convolutional neural network (CNN) for predicting average soil moisture based on images obtained from ground penetrating radar (GPR) data. The CNN is structured in two main stages: classification and regression. First, the CNN classifies GPR images of tree roots into distinct moisture content categories. Then, the pre-trained classification network is adapted using transfer learning to perform regression tasks, predicting continuous soil moisture values. To enable 3D non-invasive mapping of RZSM, we apply adaptive inverse distance weighted interpolation to reconstruct the distribution of soil water storage at various depths, ultimately generating a 3D visualization of RZSM. Finally, we validate the proposed approach using both synthetic and field data of GPR. The root mean square error between the soil moisture content predicted by this approach and the actual moisture content of the synthetic model, as well as the moisture content obtained in a field experiment, is less than 0.02 m3·m−3. This new approach for mapping RZSM holds great potential for enhancing root zone water management and promoting sustainability.
How to cite: Liu, K. and Zhao, Y.: Mapping 3D Root Zone Soil Moisture of GPR Data Based on Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5339, https://doi.org/10.5194/egusphere-egu25-5339, 2025.
Non-invasive imaging of small-scale features within the soil–plant continuum can help to advance sustainable agriculture by optimizing agricultural treatments and protecting natural resources such as groundwater and soil. This study investigates the potential of different ground penetrating radar (GPR) frequencies with two primary objectives: monitoring soil water content (SWC) variations in maize root zones and detecting soil electrical conductivity variations caused by different nitrate concentrations. Therefore, weekly horizontal crosshole GPR measurements were conducted during a maize growing season using 200 MHz and 500 MHz GPR antennae at the upper field minirhizotron facility in Selhausen, Germany. Within the facility, horizontal rhizotubes are installed in three sets of three columns, with each column containing six rhizotubes at depths ranging from 0.1 m and 1.2 m and a horizontal rhizotube spacing of 0.75 m. These were used to acquire time-lapse measurements: horizontal zero-offset profiling (ZOP) collected between 0.2 m and 1.2 m and root images at all six depths. While variations in SWC and root presence are primarily linked to the permittivity, different nitrate concentrations are expected to cause variations in soil electrical conductivity, which affects the GPR signal attenuation resulting in a lower signal amplitude in areas of higher nitrate concentrations and vice versa. The permittivity of the soil is calculated for each position using the estimated travel time and the rhizotube spacing. Variations in GPR signal amplitudes are analyzed by calculating the envelopes and identifying their maximum at each position. For a time-lapse comparison, the static and dynamic influences are removed from the permittivity and maximum envelopes by using a statistical trend-correction approach. When both data are compared along the tube, the 500 MHz provides more details and structures than 200 MHz. Pronounced root presence in the travel times are particularly evident at the 500 MHz frequency. Trend-corrected permittivity results show increased variability over time up to depths of 0.6 m and 0.8 m, correlating with greater root presence, while maximum envelopes shows greater variability only at 0.2 m. Preliminary results suggest that different nitrate concentrations affects the GPR data, with both frequencies indicating decreased maximum amplitudes in areas with higher nitrate concentration. At some locations in deeper layers, a decrease in maximum envelopes was observed while no increase in root presence was noticed, which could indicate zones of preferential flow of nitrate. The combined interpretation of permittivitiy and envelopes variations can help to disentangle the effect of SWC, roots and/or nitrate. These results highlight the potential of GPR as a non-invasive tool to accurately map root zones and to assess spatial variations in nitrate concentrations, thereby enhancing precision farming practices and promoting sustainable crop management.
How to cite: Schiebel, S., Lärm, L., Bauer, F. M., Schnepf, A., Vereecken, H., and Klotzsche, A.: Investigating the effect of maize roots under different nitrate applications using crosshole GPR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11369, https://doi.org/10.5194/egusphere-egu25-11369, 2025.
Even though availability of water and nutrients are the main limitations for grain production globally, little is known about the rooting system, the critical plant organ involved in accessing soil water and nutrients. We know that the crop’s genetic background (G), crop management (M), and the environment (E) interact to alter the architecture of the rooting system. However, root traits are hard to measure, and the lack of quick, cheap, accurate, and functional root phenotyping approaches in the field has limited the capacity of breeding, agronomy, and precision agriculture to develop traits and services for farmers. Recent advances in high-resolution root-zone soil moisture monitoring show potential to reveal genotypic and management differences in crop root systems across contrasting environments. This paper describes novel approaches for the high-throughput phenotyping of functional root traits of value for yield and yield stability. First, we introduce the phenotyping approach for in-situ 3D characterisation of sorghum water use and the root system in soil profiles. Second, we demonstrate its application to characterise two functional root traits, i.e., maximum rooting depth (MxRD), and an index of root activity (RAindex), and their phenotypic plasticities. The experiment results show that the proposed root phenotyping method could capture G´E´M effects at different crop growing stages. The plasticity of functional root traits was associated with the stability of grain yield traits. Hybrids with high root plasticity tend to have more stable grain numbers and grain weights. There is valuable genetic diversity in the mean value and plasticity of root traits that could be used to match root phenotypes to target production environments. The root phenotyping approach can be a valuable tool for understanding the dynamic interactions between root function, root architecture and yield traits in the field under variable environments.
How to cite: Zhao, D.: High-throughput root phenotyping in the field using electromagnetic induction sensors: Implications for breeding and agronomy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13598, https://doi.org/10.5194/egusphere-egu25-13598, 2025.
An accurate delineation of management zones that reliably characterizes within-field heterogeneity is essential to optimize resources and improve yields in precision agriculture. Non-invasive hydro-geophysical methods, such as electromagnetic induction (EMI), offer a rapid approach to delineating agricultural management zones that are based on subsurface soil characteristics that influence crop growth. Integrating additional data sources, such as remote sensing imagery and yield maps, can further enhance the quality and applicability of these management zones. However, integrating above-ground and subsurface information from multiple datasets for large agricultural fields poses challenges in data harmonization, analysis, and methodological consistency. Additionally, the impact of different dataset combinations on management zone delineation remains underexplored.
In this study, we propose a robust processing workflow that combines unsupervised classification and statistical validation to delineate management zones using proximal and remote sensing. This method was applied to a 70-ha field of the patchCROP experiment in Tempelberg (Germany). Part of this field consists of 30 small patches (0.5 ha each) that are managed separately since 2020. EMI data were collected in four campaigns between 2022 and 2024 by using a CMD Mini-Explorer and a CMD Mini-Explorer Special-Edition (featuring 3 and 6 coil separations, respectively). Maps of measured ECa were standardized using z-score normalization (ECaz) to reduce the effect of measuring in different environmental conditions. Additionally, seven satellite images of the 2019 growing season with 3 m resolution (PlanetScope) were used to obtain maps of NDVI development. Three dataset combinations were investigated: 1) ECaz maps, 2) NDVI maps, and 3) a combination of the EMI and NDVI maps. The Self-Organizing Maps (SOM) machine learning technique was used to cluster these three datasets. The optimal number of clusters was determined using the Multi-Cluster Average Standard Deviation (MCASD) method. Nine years (2011-2019) of yield data and detailed soil information up to 100 cm depth were used to refine the cluster numbers by using Tukey's post-hoc analysis and to assess the accuracy of the clustered maps with two-tailed t-tests in a subsequent step.
The EMI-based clustering resulted in 4 management zones. A comparison of adjacent zones showed that 15 out of 21 soil properties and 23 out of 27 yield combinations were statistically separated. The average p of all these combinations was 0.113 and 0.045, respectively. The NDVI-based clustering resulted in 3 zones with 10 out of 14 soil properties and 18 out of 18 yield combinations showing significant separation (average p of 0.166 and 0.001, respectively). Overall, the EMI-based zones better captured the patterns in soil heterogeneity, whereas the NDVI-based zones better matched yield maps. The combined EMI-NDVI clustering resulted in 3 zones, and all the combinations of soil properties and yield showed significant separation. This EMI-NDVI derived 3 m resolution map better represented soil properties and yield maps, highlighting the potential of integrating multi-source datasets for field management and, ultimately, agricultural productivity. It represents the base for actionable insights not only for precision agriculture applications such as fertilization and irrigation, but also for environmental modelling or to guide future sampling campaigns.
How to cite: Dogar, S. S., Brogi, C., O'Leary, D., Donat, M., Vereecken, H., and Huisman, J. A.: Delineating Agricultural Management Zones using Unsupervised Classification of Electromagnetic Induction and Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9440, https://doi.org/10.5194/egusphere-egu25-9440, 2025.
Leaf Area Index (LAI), the total one-sided area of leaves per unit ground area is an
important parameter in fields of science such as agriculture, ecology, forestry among
other fields of science as leaf surfaces are the main areas for energy and mass
exchange. Direct measurement of Leaf Area Index (LAI) can be destructive or time-
consuming, leading to the development of indirect methods. These approaches often
require field personnel or rely on satellites, which may have limited temporal resolution
for certain applications. Recognizing that more leaves on a plant can enhance energy
canopy interception and potentially lower soil surface energy, we aimed to explore the
relationship between Leaf Area Index (LAI) and soil temperature response. We grew
processing tomatoes (H4107) in southern Arava, Israel, and monitored Leaf Area Index
(LAI) using a Sentinel-2 based model, along with soil temperatures directly beneath the
plant at 15 cm and 30 cm depths throughout the season. In addition to a decrease in
calculated soil surface temperature amplitude with increase in LAI, the results showed a
strong linear relationship between the LAI and the minimum temperature difference
between the two depths (R 2 ~ 0.7). These findings indicate a potentially low-cost, high
temporal resolution approach to estimate LAI from soil data.
How to cite: Mugendi Muthamia, K., Berliner, P., Rozenstein, O., Tas, E., Raij Hofman, I., and Lazarovitch, N.: A Bio-Physical Model for Estimating Leaf Area Index (LAI) UsingSoil Measurements., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7257, https://doi.org/10.5194/egusphere-egu25-7257, 2025.
In precision farming, more and more methods are being developed and used for efficient and environmentally friendly farming of agricultural land. Technical solutions for rapid mapping of soil parameters help to enable more efficient field cultivation. Non-invasive methods, such as electromagnetic induction (EMI), are advantageous for fast mapping. These systems measure the electrical conductivity of the soil and enable the determination of various soil parameters (e.g. soil stratification, water content, fertilizer concentration).
For a depth-resolving measurement, which requires a large number of different coil separations and orientations, multiple surveys with different commercial EMI devices are usually necessary. To simplify the application in the field, a modular EMI system was developed that enables simultaneous measurements with flexible coil spacing.
A temperature drift correction and a model-based offset calibration were carried out as part of the measurement data pre-processing. Two approaches for calibrating the offset were tested.
In the first approach, the EMI device was positioned over a pool of water at different heights, with the water modeled as a homogeneous layer to calculate the offset. In the second approach, the system was calibrated by placing it at different heights above a natural soil of an agricultural area.
To evaluate the quality of the EMI measurements, the apparent soil conductivity was mapped with the SELMA-RB system (twelve separations) and a commercial CMD measuring system (six separations, CMD Mini Explorer) on a test field (230 m x 160 m) near Jülich, Germany. The field was measured within approximately one hour by pulling the device with an ATV at 6-8 km/h with 4 m line spacing. A comparison of the conductivity maps and the calibration data are presented.
How to cite: Dick, M., Egon, Z., Johan Alexander, H., Achim, M., Peter, W., Michael, R., Benedikt, S., Julie, B., Salar Saeed, D., Cosimo, B., and Ghaleb, N.: Calibration and Field Measurements of a Scalable Electromagnetic Induction System (SELMA-RB) for Agricultural Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6817, https://doi.org/10.5194/egusphere-egu25-6817, 2025.
Agricultural water management is becoming an increasingly actual issue in a period of severe climate changings. Sustainable water management requires adequate knowledge of soil water availability and its storage capacity.
The correct management of irrigation water requires a good knowledge of the volumetric water content (VWC) in the soil. VWC is a parameter that can be measured using soil humidity sensors, and it can help understanding how the irrigation water distributes in the soil. However, these sensors are point humidity monitoring systems, that means they provide information limited to the vertical where they are installed and do not allow a 2D or 3D reconstruction of the water content in the subsoil. As well known, Electrical Resistivity Tomographies (ERT), a non-invasive geophysical method, estimates the spatial and temporal variations of soil resistivity (and thus of its inverse, i.e., conductivity), which is linked to parameters such as water content. Unlike point-based soil moisture sensors, ERT provides a broader view of water distribution in the soil. Thus, the goal of this study was to use electrical conductivity (EC) by full 3D-ERTs and moisture sensors to estimate the volumetric water content in the soil.
The study was conducted in a field dedicated to melon cultivation, where a detailed study of the irrigation system has been carried out over the years. It was determined that the three-drip-line system with a capacity of 4.1 lh/m² is the best irrigation system for this field located in Braccagni (GR, Italy). Therefore, one plot of the field was equipped with a three-drip-line irrigation system, and 72 electrodes were installed to perform full 3D-ERT measurements. Additionally, two PVC tubes, sealed at the base and with an opening at the surface, were installed to allow the insertion of the Diviner2000 probe and to measure soil moisture every 10 cm down to a maximum depth of 70 cm. Two ECH2O 10 HS sensors were also installed, connected to a data logger capable of recording temperature and moisture measurements every 30 min. The sensors, 15 cm in length, were installed vertically in the soil, allowing the measurement of VWC in a soil volume of 0.001 m³.
Between June and August 2023, six measurement campaigns of electrical conductivity were conducted. It is known that there is a direct relationship between EC and VWC. Therefore, the VWC data recorded by the Diviner2000 for all six acquisition times were correlated with the EC data acquired by ERTs. The two datasets (EC from ERT and VWC from Diviner2000) are in perfect agreement, showing a linear relationship with a R² of 0.96. Using the obtained regression law, it is possible to convert EC tomographies into VWC tomographies, thereby visualizing the variation of water content in the subsoil. This made it possible to understand the water distribution within the plot and to determine the percentage of water present throughout the entire root zone, not just at the points where the moisture sensors are installed.
How to cite: Innocenti, A., Fanti, R., and Pazzi, V.: Transforming electrical resistivity tomographies into volumetric water content ones: a strategy for optimizing irrigation in horticulture., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16636, https://doi.org/10.5194/egusphere-egu25-16636, 2025.
Geophysical methods are non-invasive techniques employed to measure the physical properties of the investigated media—primarily electrical and mechanical—while preserving the dynamics of soil structure without altering its state. These methods can be used qualitatively to detect soil anomalies and spatial heterogeneities, as well as quantitatively to correlate primary soil properties with physical measurements. Soil compaction resulting from traffic with modern agricultural machinery has significantly increased, leading to substantial impacts on soil ecosystem services and crop yields.
The quantification of soil structure and compaction has traditionally been performed through destructive soil sampling followed by, dry bulk density and porosity measurements, or through inferential methods (e.g., pedotransfer functions).
This study investigated the potential of an integrated geophysical approach using autonomous driving rover (Robotti 150D, Agrointelli-DK) for mapping soil variability and compaction status on arable land.
The experiment was conducted at the L. Toniolo experimental farm of the University of Padua on a 1-ha field comprising a complete randomize design testing two traffic treatments (conventional and controlled traffic with autonomous guidance vehicle) and four replicates covering 8 plots (130 m x 10 m). The geophysical instruments mounted on the rover included: a γ-ray detector (Agri Detector MS-2000, Medusa - NL) positioned at the front; a GPR (Stream DP, IDS - IT) and a cosmic ray neutron sensing probe (Finapp - IT) in the central section; and an electromagnetic conductivity meter (CMD-MiniExplorer, GF Instruments - CZ) mounted on a wooden sled at the rear. Measurements were conducted at a speed of 3.6 km h⁻¹, with swaths spaced every two meters. All instruments operated simultaneously and were connected to a GPS equipped with an RTK positioning system, ensuring a precision of 2 cm.
Moreover, eight (1 per plot) 3D electrical conductivity tomographies (ERTs) (Syscal Terra, IRIS - FR) were performed for each replicate on a ca. 3 m3 investigated volume (4.6 × 0.8 × 0.8 m) using a dipole-dipole array. Geophysical techniques were then complemented by traditional destructive measurements of bulk density (core method), soil penetration resistance (Eijkelkamp - NL) and soil texture on the top 1 m and by drone surveys to create a digital elevation model (DEM).
Preliminary results demonstrated that the combination of an autonomous robot with several multi-layer geophysical sensors can act as a proxy for expeditive digital soil mapping on large surfaces. Nevertheless, the ERT capability to capture the presence of resistivity anomalies and its combination with traditional method seemed fundamental to precisely adjust the multi-mapping survey.
In conclusion, the tested approach might provide a consistent set of real time data valuable also for training machine learning algorithms and give new insight to precision agriculture technique.
How to cite: Gabrieli, D., Piccoli, I., Gasparini, F., Sartori, L., and Morari, F.: Advancing Field-Scale Soil Mapping Using An Autonomous Rover With Multi-Layer Geophysical Sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19478, https://doi.org/10.5194/egusphere-egu25-19478, 2025.
Emissions of greenhouse gases from the agricultural sector vary in space and time, leading to hot spots and hot moments with large variability between farms. Large hot spots can exhibit enhancements of only a few ppbv above background and last on time scales of hours to days. For constraining N2O emissions and developing reduction strategies, detailed source characterization on emissions (impacts of fertilization type and timing, agricultural practices, soil conditions, etc.) is required. To this end, we developed and deployed in a soybean field a laser tomographic imaging system for N2O mapping and associated emissions quantification. A pair of continuous wave quantum cascade lasers scan across a field to an array of inexpensive mid-IR reflectors lining its perimeter, casting an optical web over an agricultural field. Each laser is tower mounted with a gimbal to aim the beams at various retroreflectors spread around the perimeter of the agricultural field. Each scan takes ~37 minutes and operates autonomously. Multiple retroreflectors are needed to create the high-resolution optical web, but traditional retroreflector cubic prisms are too bulky, expensive, and delicate for such field use. Consequently, we developed custom, thin (4 mm thick) plastic retroreflectors for the mid-IR with reflectivities reaching ~86% which are broadband across the entire infrared and perform better than traditional corner cubes. FPGA electronics ensure a low power (25 W/tower) system for remote field use. In the deployed configuration, 32 path-integrated overlapping measurements from two separate laser towers are combined for full mapping of N2O over the field through a computed tomographic reconstruction. A Monte Carlo approach is used for inversion modeling to locate the plume location to within two meters and estimate the emission rate to within 10% on acre sized fields. The same techniques and tools developed can readily be adapted to other gases like methane and ammonia for other applications in agricultural and industrial settings.
How to cite: Sevostianov, V., Guiguizian, P., Collins, J., and Zondlo, M.: Continuous Spatiotemporal Sensing of N2O through an Optical Web, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2603, https://doi.org/10.5194/egusphere-egu25-2603, 2025.
Geophysical surveys, particularly with ground-penetrating radar (GPR), have been proven useful tools for the detection and mapping of tile drainage in agricultural fields (Wienken & Grenzdorffer, 2024). However, the success of a GPR survey for this purpose depends on both the characteristics of the tile drain pipes, such as their material, diameter, and depth – which are often poorly documented – as well as environmental conditions, such as soil texture and moisture content. Furthermore, these environmental conditions can be highly variable in space and dynamic over time, adding to the challenge of assessing in advance whether a GPR survey will be worth the investment.
To assess the likelihood of successfully detecting tile drainage networks before planning a field survey, we developed a synthetic modelling framework using the open-source software gprMax (Warren et al., 2016). The framework evaluates how selected parameters influence the GPR signal, focusing on the reflection contrast expected when the electromagnetic wave interacts with a drainpipe in a simplified one-dimensional (1D) model. Whether detection is possible is determined by comparing the simulated reflection contrast with a general noise threshold typical for a time-domain GPR system with a specified centre frequency. In this study, all synthetic modelling tests were performed for a GPR system with a centre frequency of 300 MHz.
We explored the sensitivity of the GPR signal to soil texture, soil moisture content, as well as the radius, depth, and filling of the drainpipe, considering a laterally homogeneous soil profile composed of one or two layers. The validity of the modelling framework was assessed by comparing the predicted detectability with the detection success/failure in two real field cases with sandy and clayey soil types. While the synthetic model predicted feasible detection for the sandy field, no clear contrasts were visible in the radargrams after basic processing. This suggests the need for further refinement of the synthetic model, such as incorporating more complex soil variations and a more detailed representation of the drainpipe structure. Nevertheless, the modelling framework provides useful guidelines for planning and designing GPR field surveys, without requiring extensive prior information on site conditions.
Further research is recommended to explore additional centre frequencies, more complex soil structures, and the incorporation of higher-dimensional approaches (2D or even 3D) to extend the current modelling framework. However, it should balance complexity with practical applicability, as real field conditions are never entirely predictable and models must simplify certain aspects due to incomplete knowledge.
References
Warren, C., Giannopoulos, A., & Giannakis, I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 209, 163–170. https://doi.org/10.1016/j.cpc.2016.08.020
Wienken, J. S., & Grenzdorffer, G. J. (2024). Non-invasive detection methods for subsurface drainage systems: A comparative review. Agricultural Water Management, 304, 109099. https://doi.org/10.1016/j.agwat.2024.109099
How to cite: Van De Vijver, E., Vanrietvelde, S., De Weerdt, P., Cornelis, W., and De Smedt, P.: A modelling framework for the preliminary assessment of tile drainage detection using ground-penetrating radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18694, https://doi.org/10.5194/egusphere-egu25-18694, 2025.
Agricultural soil monitoring is essential for fostering sustainable farming practices and safeguarding environmental health, particularly in productive regions like the Ferrara alluvial plain in the Po Valley. This area, renowned for its rich agricultural heritage, faces increasing vulnerabilities due to climate change and human activities. Challenges include frequent droughts, overexploitation of soils, and unsustainable farming practices, which lead to soil degradation, reduced crop yields, and elevated greenhouse gas emissions. Traditional soil monitoring methods often lack the spatial and temporal insights needed to address these issues effectively, limiting farmers’ ability to implement conservation strategies.
To address these challenges, there is growing emphasis on integrating geophysical methods with geochemical analyses to enhance soil characterization and monitoring at the field scale. Geophysical techniques such as Electrical Resistivity Tomography (ERT), Electromagnetic Induction (EMI), and Ground-Penetrating Radar (GPR) provide non-invasive, in-situ assessments of soil properties, including moisture content, porosity, and soil structure. These methods efficiently characterize large agricultural areas, offering insights to depths of 150 cm at a relatively low cost.
Complementary geochemical analyses of soil samples from specific horizons (e.g., 0–50 cm, 50–100 cm) offer detailed data on soil salinity, organic matter content, and isotopic signatures. This information helps assess salinity impacts, trace organic matter depletion, and evaluate nutrient loss. However, geochemical sampling is limited by its localized scope and costs. In contrast, geophysical methods offer broader spatial coverage and high spatial resolution, enabling the creation of detailed 2D and 3D maps. Nonetheless, they are less precise in quantifying specific properties, highlighting the need for a combined approach that leverages both methodologies.
This integrated approach was applied to agricultural lands in Ferrara province, focusing on reclaimed lowlands near the Adriatic Sea with peaty soils particularly vulnerable to salinity. Geophysical analysis, conducted with an EM-400 Profiler, was paired with laboratory-based geochemical analyses (EA-IRMS and GroLine portable hydroponic probe) to gain a comprehensive understanding of soil conditions. The study correlated geophysical parameters, such as electrical conductivity, with geochemical results to depict spatial soil variations.
This methodology supports precision agriculture by optimizing irrigation schedules and fertilizer application based on spatially explicit electrical conductivity data. Such practices enhance resource use efficiency, reduce environmental degradation, and promote sustainable soil and water management. Moreover, the approach aids in designing remediation strategies for contaminated sites, improving soil quality and environmental health.
In the Ferrara plain and similar areas, this synergistic methodology equips stakeholders with tools to address interconnected challenges like climate change, salinization, organic matter degradation, and fertility decline. It provides essential insights for informed agricultural management, ensuring long-term sustainability in vulnerable landscapes.
This work is supported by the Emilia-Romagna Region fund “Territorio: transizione tecnologica, culturale, economica e sociale verso la sostenibilità pr fse+ 2021/2027 priorità 2.”
How to cite: Sobbe, A., Rizzo, E., and Bianchini, G.: Geophysical and geochemical data integration for agricultural soil monitoring andprevention of the effects of salinity and soil organic matter in the Province of Ferrara (Northern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8196, https://doi.org/10.5194/egusphere-egu25-8196, 2025.
The soil is an essential natural resource that supports agriculture and forestry, and plays a crucial role in global hydrological processes. Traditional methods used to study soil properties are commonly based on laboratory measurements on core samples, sporadic field measurements on soil profiles or visual evaluation of soil traits. These methods provide detailed information of soil physical properties, yet they offer limited capabilities to quantify and monitor spatial and temporal variations about soil physical properties. Geoelectrical methods, due to their non-invasive nature, sensitivity to soil physical properties and robustness in their application, are increasingly used to complement traditional observations and fill spatial and temporal gaps of information on soil properties.
In this work, we present a case study of using geoelectric methods to investigate soil compaction. We measured Electrical Resistivity Tomography (ERT) data before and after an experimental soil compaction event and for two different levels of compaction (ten passages of a five-ton tractor and 4 passages of a ten-ton vehicle) in an agricultural field. The field of study was a grassland, that had remain unmanaged for approximately four decades, located in the Santa Escolástica agricultural site, in San Antonio de Areco, Buenos Aires, Argentina. We collected two (7.75m long and 0.25m electrode spacing) ERT transects (before and after the compaction event) along the wheel tracks, and a third similar transect perpendicular to the wheel tracks (only after compaction). In addition, a soil pit was dug to conduct a visual analysis of the soil layering.
The ERT transects were independently inverted using the res2dinv software to obtain an image of the electrical resistivity of the three soil profiles. Results indicate a reduction in soil electrical resistivity of up to 25% in the top soil after the 4 passages of the 10 ton vehicle and 20% for the 10 passages of the 5 ton tractor. Correspondingly, in the upper subsoil layer at a depth of 0.55 m, we estimated a reduction of up to 10% for the first compaction case and negligible reduction for the second compaction treatment. Ongoing and future work will focus on enhancing the inversion results by incorporating geometrical constrains and simultaneously collected ground penetrating radar data.
How to cite: Gomez, A. G., Perdomo, S., Romero Ruiz, A., Rubino, G., and Lissa, S.: Soil-compaction imaging using geoelectrical methods in a grassland field of Buenos Aires, Argentina., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12387, https://doi.org/10.5194/egusphere-egu25-12387, 2025.
Geophysical prospection offers non-invasive tools to investigate subsurface properties, that can be particularly useful in agricultural contexts. This study focuses on an agricultural field managed by INRAE (French National Research Institute for Agriculture, Food and Environment), under a Mediterranean climate (Avignon, FR). The site benefits from characterization of lithological profiles, soil physico-chemical analyses, and continuous monitoring of groundwater table depth as well as soil water content at a few depths.
We employed Electrical Resistivity Tomography (ERT) and seismic methods to develop 1D and 2D profiles of resistivity and seismic wave velocities (Vp and Vs, for the compressional and shear waves, respectively). Apparent resistivity data from ERT were inverted using pyGIMLi to generate 2D resistivity models, while first-arrival travel times from seismic data were similarly inverted with pyGIMLi to produce Vp profiles. Surface wave were analysed by Multichannel Analysis of Surface Waves (MASW) to derive Vs through dispersion curve inversion following the SWIP workflow developed by Pasquet and Bodet (2017). These profiles provide insights into subsurface structure and heterogeneity, reflecting variations in soil and lithological properties, as well as water content variation.
While this study focuses on presenting resistivity, Vp, and Vs profiles, the integration and joint inversion of these seimic and resistivity datasets for detailed hydrological and geomechanical characterization is planned as part of future work in this doctoral research. This approach aims to enhance our understanding of water distribution and soil mechanical properties in agricultural environments.
How to cite: Pellorce, L., Valois, R., Doussan, C., and Mesgouez, A.: Time-lapse resistivity and seismic profiles (Vp, Vs) for Subsurface Characterization: A Case Study in a Well-Documented Agricultural Field, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11068, https://doi.org/10.5194/egusphere-egu25-11068, 2025.
