Pore Structure Analysis of Growing Media Using X-Ray Microcomputed Tomography
Source abstract
Abstract
This study investigated the representative elementary volume (REV) for visible porosity in horticultural growing media (peat, commercial mixture, treated wood fibre/peat, pure wood fibre) using x-ray micro-computed tomography (µCT) with 2D and 3D image division, pore morphology, water retention curve (WRC), and saturated hydraulic conductivity (Ksat) via pore network modelling (PNM). Two sample sizes (10 × 10 cm, 3 × 3 cm, height × diameter) with resolutions of 46 and 15 µm were analysed. REV was assessed using deterministic (dREV) and statistical (sREV) criteria, evaluating the porosity and coefficient of variation across subvolumes. The results showed that 3D division of large samples achieved REV only for pure wood fibre (8000–10,000 µm), while 2D division met both criteria for all media. For small samples, 3D division achieved REV only for wood fibre/peat mixture, but 2D division succeeded for all media above 3000 µm. Pore analyses indicated that pure wood fibre had the largest, most connected pores, enhancing drainage, while peat showed complex, retentive structures. WRCs aligned well with lab data (R2 > 0.88). PNM Ksat estimates from small images were more accurate, with discrepancies (21–172%) due to segmentation artefacts. Future studies should incorporate permeability or tortuosity and explore multiscale imaging for improved hydrophysical predictions. This study also highlights advantages unique to X-ray µCT compared to standard laboratory methods, e.g., direct three-dimensional quantification of pore structure parameters and an image-based determination of the REV.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wood Treatment and Properties · Heat and Mass Transfer in Porous Media · Advanced Cellulose Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Agricultural Sciences and Landscape Architecture, Osnabrueck University of Applied Sciences, Am Kruempel 31, 49090 Osnabrück, Germany
Institute for Crop and Soil Science, Julius Kühn (JKI), Bundesallee 58, 38116 Braunschweig, Germany
Funding
German Federal Ministry of Food and Agriculture · FNR
References (92)
Show source-provided references
1. Applications of X-ray computed tomography for examining biophysical interactions and structural development in soil systems: A review10.1111/ejss.12028 ↗
2. Characterizing soil pore structure using nitrogen adsorption, mercury intrusion porosimetry, and synchrotron-radiation-based X-ray computed microtomography techniques10.1007/s11368-014-0995-0 ↗
3. Quantification of soil structure based on Minkowski functions10.1016/j.cageo.2010.03.007 ↗
4. Hydraulic conductivity and pore connectivity. Effects of conventional and no-till systems determined using a simple laboratory device10.1016/j.geoderma.2018.10.045 ↗
5. Integral parameters for characterizing water, energy, and aeration properties of soilless plant growth media10.1016/j.jhydrol.2013.08.031 ↗
6. Fussy, A., and Papenbrock, J. (2022). An overview of soil and soilless cultivation techniques—Chances, challenges and the neglected question of sustainability. Plants, 11.10.3390/plants11091153 ↗
7. Organic growing media: Constituents and properties10.2136/vzj2014.09.0125 ↗
8. Kormanek, M., Małek, S., Banach, J., and Durło, G. (2023). Effect of changing substrate density and water application method on substrate physical properties and container-grown seedling growth. Forests, 14.10.3390/f14071490 ↗
9. Characterising and linking X-ray CT derived macroporosity parameters to infiltration in soils with contrasting structures10.1016/j.geoderma.2017.10.020 ↗
10. Characterization of Micro-Scale Pore Structure and Permeability Simulation of Peat Soil Based on 2D/3D X-ray Computed Tomography Images10.1134/S1064229322060060 ↗
11. (1998). Methods of Soil Investigations for Agricultural Engineering—Physical Laboratory Tests-Part9: Determination of the Saturated Hydraulic Water Conductivity in the Cylindrical Core-Cutter (Standard No. DIN 19683).
12. (2011). Soil Improvers and Growing Media-Determination of Physical Properties-Dry Bulk Density, Air Volume, Water Volume, Shrinkage Value and Total Pore Space (Standard No. DIN EN 13041).
13. (2019). Soil Quality—Determination of the Water-Retention Characteristic—Laboratory Methods (Standard No. DIN EN ISO 11274).
14. Muhammed, H.H., Anlauf, R., and Daum, D. (2025). Estimation of Hydraulic Properties of Growing Media from Numerical Inversion of Mini Disk Infiltrometer Data. Hydrology, 12.10.3390/hydrology12050100 ↗
15. Muhammed, H.H., Schumm, L., Anlauf, R., Reineke, T., and Daum, D. (2024). Validity of a Centrifuge-Based Method for Determining the Water Retention Curve of Growing Media. Horticulturae, 10.10.20944/preprints202410.0775.v1 ↗
16. X-ray computed microtomography for the study of the soil–root relationship in grassland soils10.2136/vzj2013.01.0014 ↗
17. Grassland management intensity determines root development, soil structure, and their interrelationship: Results of a regional study of Leptosols in the Swabian Alps10.1002/glr2.12077 ↗
18. Soil pore structure dynamics under steady-state wetting-drying cycle10.1016/j.geoderma.2023.116401 ↗
19. Quantification of the pore size distribution of soils: Assessment of existing software using tomographic and synthetic 3D images10.1016/j.geoderma.2017.03.025 ↗
20. A simple model to predict hydraulic conductivity in medium to dry soil from the water retention curve10.1029/2020WR029211 ↗
21. X-ray microtomography analysis of soil pore structure dynamics under wetting and drying cycles10.1016/j.geoderma.2019.114103 ↗
22. Clarifying pore diameter, pore width, and their relationship through pressure measurements: A critical study10.1016/j.marpetgeo.2019.05.019 ↗
23. Comparison of soil tortuosity calculated by different methods10.1016/j.geoderma.2021.115358 ↗
24. Soil pore characteristics assessed from X-ray micro-CT derived images and correlations to soil friability10.1016/j.geoderma.2012.02.024 ↗
25. Morphology and multifractal characteristics of soil pores and their functional implication10.1016/j.catena.2020.104822 ↗
26. Multifractal analysis of 3D images of tillage soil10.1016/j.geoderma.2017.02.013 ↗
27. Multifractal analysis on CT soil images: Fluctuation analysis versus mass distribution10.1016/j.chaos.2023.114080 ↗
28. X-ray computed tomography: Validation of the effect of pore size and its connectivity on saturated hydraulic conductivity
29. X-ray microtomography analysis of representative elementary volume (REV) of soil morphological and geometrical properties10.1016/j.still.2018.05.004 ↗
30. Gaspareto, J.V., de Oliveira, J.A.T., Andrade, E., and Pires, L.F. (2023). Representative Elementary Volume as a Function of Land Uses and Soil Processes Based on 3D Pore System Analysis. Agriculture, 13.10.3390/agriculture13030736 ↗
31. Scale and REV analyses for porosity and pore connectivity measures in undisturbed soil10.1016/j.geoderma.2020.114206 ↗
32. Quantifying Physical Properties of Three Sphagnum-Based Growing Media as Affected by Drying–Wetting Cycles10.2136/vzj2019.04.0033 ↗
33. Measuring physical and hydraulic properties of peat from X-ray tomography10.1016/j.geoderma.2009.08.010 ↗
34. VDLUFA (1991). Methods Book I “Soil Analysis” (1st–6th Supplement Delivery), VDLUFA-Verlag. [4th ed.].
35. (2009). Soil Improvers and Growing Media—Determination of Organic Content and Ash. German Version (Standard No. DIN EN 13039).
36. Aggregate stability and size distribution10.2136/sssabookser5.1.2ed.c17 ↗
37. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils10.2136/sssaj1980.03615995004400050002x ↗
38. SciPy 1.0: Fundamental algorithms for scientific computing in Python10.1038/s41592-019-0686-2 ↗
39. scikit-image: Image processing in Python10.7717/peerj.453 ↗
40. Array programming with NumPy10.1038/s41586-020-2649-2 ↗
41. PoreSpy: A python toolkit for quantitative analysis of porous media images10.21105/joss.01296 ↗
42. OpenPNM: A pore network modeling package10.1109/MCSE.2016.49 ↗
43. Kettridge, N., and Binley, A. (2011). Characterization of peat structure using X-ray computed tomography and its control on the ebullition of biogenic gas bubbles. J. Geophys. Res. Biogeosci., 116.10.1029/2010JG001478 ↗
44. San José Martínez, F., Martín, L., and García-Gutiérrez, C. (2018). Minkowski functionals of connected soil porosity as indicators of soil tillage and depth. Front. Environ. Sci., 6.10.3389/fenvs.2018.00055 ↗
45. Contrast-enhanced repacked soil cores as a proxy for soil organic matter spatial arrangement10.1071/SR18191 ↗
46. Determination of the air void content of asphalt concrete mixtures using artificial intelligence techniques to segment micro-CT images10.1080/10298436.2021.1931197 ↗
47. The impact of manual threshold selection in medical additive manufacturing10.1007/s11548-016-1490-4 ↗
48. Representative Elementary Volume in Agricultural Soils: Effects of Image Resolution and Tillage Practices on Pore Structure10.1016/j.soilad.2025.100081 ↗
49. Matplotlib: A 2D graphics environment10.1109/MCSE.2007.55 ↗
50. Gohlke, C. (2025). Cgohlke/Tifffile, v2025.6.11, Zenodo.
51. A comprehensive survey on impulse and Gaussian denoising filters for digital images10.1016/j.sigpro.2018.12.006 ↗
52. Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications
53. Identifying the representative elementary volume for permeability in heterolithic deposits using numerical rock models10.1007/s11004-008-9182-4 ↗
54. A new criterion for determining the representative elementary volume of translucent porous media and inner contaminant10.5194/hess-24-5903-2020 ↗
55. Computed tomography to estimate the representative elementary area for soil porosity measurements10.1100/2012/526380 ↗
56. Pore scale study of flow in porous media: Scale dependency, REV, and statistical REV10.1029/1999GL011101 ↗
57. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations10.1016/0021-9991(88)90002-2 ↗
58. Sethian, J.A. (1999). Level Set Methods and Fast Marching Methods, Cambridge UP.10.1137/S0036144598347059 ↗
59. Pabst, W., and Gregorová, E. (2025, August 13). Characterization of Particles and Particle Systems. Available online: https://old.vscht.cz/sil/keramika/Characterization_of_particles/CPPS%20_English%20version_.pdf.
60. Connectivity metrics for subsurface flow and transport10.1016/j.advwatres.2011.12.001 ↗
61. Quantification of the structure evolution in a garden soil over the course of two years10.1016/j.geoderma.2018.12.030 ↗
62. Permeability of porous media: Role of the critical pore size10.1002/2016JB013793 ↗
63. Hillel, D. (2004). Introduction to Environmental Soil Physics, Elsevier Academic Press.
64. Bouix, S., and Siddiqi, K. (2000). Divergence-Based Medial Surfaces, Springer.10.1007/3-540-45054-8_39 ↗
65. Shape factor and hydraulic conductance in noncircular capillaries: I. One-phase creeping flow10.1006/jcis.2000.7413 ↗
66. Analysis of Tomographic Images of the Soil Pore Space Structure by Integral Geometry Methods10.1134/S1064229321090039 ↗
67. Predicting Solute Transport in Structured Soil Using Pore Network Models10.2136/vzj2010.0158 ↗
68. Predictive pore-scale modeling of two-phase flow in mixed wet media10.1029/2003WR002627 ↗
69. Pore-Size Distributions of Soils Derived using a Geometrical Approach and Multiple Resolution MicroCT Images10.2136/sssaj2016.09.0291 ↗
70. Coupling X-ray microtomography and macroscopic soil measurements: A method to enhance near-saturation functions?10.5194/hess-18-1805-2014 ↗
71. Identification of pore spaces in 3D CT soil images using PFCM partitional clustering
72. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems10.1016/j.advwatres.2012.07.018 ↗
73. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images10.1016/j.geoderma.2013.05.017 ↗
74. X-ray µCT: How soil pore space description can be altered by image processing10.2136/vzj2016.06.0049 ↗
75. The effects of dual porosity on transport and retardation in peat: A laboratory experiment10.4141/cjss2011-050 ↗
76. 3D image-based characterization of fluid displacement in a Berea core10.1016/j.advwatres.2005.05.015 ↗
77. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review10.1016/j.advwatres.2006.05.025 ↗
78. Analysing the impact of compaction of soil aggregates using X-ray microtomography and water flow simulations10.1016/j.still.2015.02.004 ↗
79. Links between soil pore structure, water flow and solute transport in the topsoil of an arable field: Does soil organic carbon matter?10.1016/j.geoderma.2024.117001 ↗
80. Soil pore structure mediates the effects of soil oxygen on the dynamics of greenhouse gases during wetting–drying phases10.1016/j.scitotenv.2023.165192 ↗
81. Porous media characterization using Minkowski functionals: Theories, applications and future directions10.1007/s11242-018-1201-4 ↗
82. Water retention, gas transport, and pore network complexity during short-term regeneration of soil structure10.2136/sssaj2013.07.0270 ↗
83. Three-dimensional quantification of intra-aggregate pore-space features using synchrotron-radiation-based microtomography10.2136/sssaj2007.0130 ↗
84. Hou, X., Qi, S., and Liu, F. (2023). Soil water retention and pore characteristics of intact loess buried at different depths. Sustainability, 15.10.3390/su152014890 ↗
85. Pore-scale imaging and modelling10.1016/j.advwatres.2012.03.003 ↗
86. Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data10.1016/j.geoderma.2010.03.015 ↗
87. Gackiewicz, B., Lamorski, K., Sławiński, C., Hsu, S.-Y., and Chang, L.-C. (2021). An intercomparison of the pore network to the Navier–Stokes modeling approach applied for saturated conductivity estimation from X-ray CT images. Sci. Rep., 11.10.1038/s41598-021-85325-z ↗
88. Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport10.1016/j.jconhyd.2016.07.002 ↗
89. Modelling hydraulic conductivity of porous media using machine learning techniques10.2166/hydro.2025.161 ↗
90. A framework for multiphase pore-scale modeling based on micro-CT imaging10.1007/s11242-025-02156-6 ↗
91. Comparative analysis on pore-scale permeability prediction on micro-CT images of rock using numerical and empirical approaches10.1002/ese3.465 ↗
92. Optimal X-ray micro-CT image based methods for porosity and permeability quantification in heterogeneous sandstones10.1093/gji/ggaa321 ↗