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Plant Science Today (PST)

Review Articles

Vol. 13 No. sp9 (2026): Recent Advances in Agriculture

Emerging paradigm in sensor based pedology: Advances, applications, challenges and future perspectives

DOI
https://doi.org/10.14719/pst.16488
Submitted
2 July 2026
Published
23-10-2026

Abstract

Pedology has seen a significant revolution due to the adoption of modern sensors that can provide fast, non-invasive and high resolution measurements of soils. Traditional pedological surveys that rely on field work and laboratory analysis are efficient, yet time consuming, cumbersome and have exhibit low spatial resolution. Modern advancements in sensors that measure optical, geophysical, elemental, moisture and remote sensing characteristics of soils have considerably improved the evaluation of soil’s physical, chemical and morphological characteristics. This article provides a detailed overview of emerging sensor-based techniques in pedology including the principles of these techniques, their applications and limitations. The review further examines the roles of digital soil mapping (DSM), proximal soil sensing, Geographic Information Systems (GIS), machine learning and artificial intelligence in enhancing soil property prediction and spatial modelling. This review also highlights the significance of calibration and validation of sensors and ground truthing of observations. It critically analyses the problems associated with the calibration transferability, soil heterogeneity, moisture effects and cost of sensors. Recent advances in sensor fusion, internet of things (IoT)-enabled monitoring, hyperspectral imaging and explainable artificial intelligence are also discussed as promising directions for future soil assessment. Furthermore, major research gaps including the need for standardised calibration protocols, regional soil spectral libraries, affordable sensing technologies and interpretable machine-learning models are identified. Overall, sensor-based pedology complements conventional soil survey methods by providing accurate, efficient and spatially continuous soil information thereby supporting sustainable soil management, precision agriculture, environmental monitoring and evidence-based land-use planning under changing climatic conditions.

References

  1. 1. Jenny H. Factors of soil formation: a system of quantitative pedology. Courier Corporation; 1994.
  2. 2. Breemen N, Buurman P. Soil formation. 2nd ed. New York: Laboratory of Soil Science and Geology; 2003. p. 239–40.
  3. 3. Anderson S. Soils: genesis and geomorphology. Cambridge: Cambridge University Press; 2005.
  4. 4. Goovaerts P. Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biology and Fertility of Soils. 1998;27(4):315–34. https://doi.org/10.1007/s003740050439
  5. 5. MacMillan R, Pettapiece W, Brierley J. An expert system for allocating soils to landforms through the application of soil survey tacit knowledge. Canadian Journal of Soil Science. 2005;85(1):103–12. https://doi.org/10.4141/S04-029
  6. 6. Thingujam U, Prabha D, Ghosh Bag A, Thingujam V, Darshan N, Dutta S. From point sensing to intelligent systems: a comprehensive review on advanced sensor technologies for soil health monitoring. Discover Sensors. 2025;1(1):27. https://doi.org/10.1007/s44397-025-00028-8
  7. 7. Adeniyi OD, Bature H, Mearker M. A systematic review on digital soil mapping approaches in lowland areas. Land. 2024;13(3):379. https://doi.org/10.3390/land13030379
  8. 8. Rossel RV, Adamchuk V, Sudduth K, McKenzie N, Lobsey C. Proximal soil sensing: an effective approach for soil measurements in space and time. Advances in Agronomy. 2011;113:243–91. https://doi.org/10.1016/B978-0-12-386473-4.00005-1
  9. 9. Minasny B, McBratney AB. Digital soil mapping: a brief history and some lessons. Geoderma. 2016;264:301–11. https://doi.org/10.1016/j.geoderma.2015.07.017
  10. 10. Ma Y, Minasny B, Malone BP, McBratney AB. Pedology and digital soil mapping (DSM). European Journal of Soil Science. 2019;70(2):216–35. https://doi.org/10.1111/ejss.12790
  11. 11. McBratney AB, Santos MM, Minasny B. On digital soil mapping. Geoderma. 2003;117(1-2):3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
  12. 12. Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture. 2004;44(1):71–91. https://doi.org/10.1016/j.compag.2004.03.002
  13. 13. Rossel RV, Walvoort D, McBratney A, Janik LJ, Skjemstad J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma. 2006;131(1-2):59–75. https://doi.org/10.1016/j.geoderma.2005.03.007
  14. 14. Stenberg B, Rossel RAV, Mouazen AM, Wetterlind J. Visible and near infrared spectroscopy in soil science. Advances in Agronomy. 2010;107:163–215. https://doi.org/10.1016/S0065-2113(10)07005-7
  15. 15. Brus D. Statistical sampling approaches for soil monitoring. European Journal of Soil Science. 2014;65(6):779–91. https://doi.org/10.1111/ejss.12176
  16. 16. Nocita M, Stevens A, van Wesemael B, Aitkenhead M, Bachmann M, Barthès B, et al. Soil spectroscopy: an alternative to wet chemistry for soil monitoring. Advances in Agronomy. 2015;132:139–59. https://doi.org/10.1016/bs.agron.2015.02.002
  17. 17. Koganti T, De Smedt P, Farzamian M, Knadel M, Triantafilis J, Christiansen AV, et al. Digital soil mapping using electromagnetic sensors. Frontiers Media SA. 2024. p. 1536797. https://doi.org/10.3389/fsoil.2024.1536797
  18. 18. Padarian J, Minasny B, McBratney AB. Machine learning and soil sciences: a review aided by machine learning tools. Soil. 2020;6(1):35–52. https://doi.org/10.5194/soil-6-35-2020
  19. 19. Swanhart S, Weindorf DC, Chakraborty S, Bakr N, Zhu Y, Nelson C, et al. Soil salinity measurement via portable X-ray fluorescence spectrometry. Soil Science. 2014;179(9):417–23. https://doi.org/10.1097/SS.0000000000000088
  20. 20. Al-Najjar O, Wudil Y, Ahmad U, Baghabra Al-Amoudi OS, Al-Osta MA, Gondal M. Applications of laser induced breakdown spectroscopy in geotechnical engineering: a critical review of recent developments, perspectives and challenges. Applied Spectroscopy Reviews. 2023;58(10):687–723. https://doi.org/10.1080/05704928.2022.2136192
  21. 21. Zeyliger A, Ermolaeva O. Development of geophysical moisture measurement methods for soil moisture mapping at agricultural field scale within the framework of digital irrigation. 2025.
  22. 22. Viscarra Rossel RA, Webster R, Bui EN, Baldock JA. Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change. Global Change Biology. 2014;20(9):2953–70. https://doi.org/10.1111/gcb.12569
  23. 23. Rossel RV, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma. 2010;158(1-2):46–54. https://doi.org/10.1016/j.geoderma.2009.12.025
  24. 24. Shin SK, Lee SJ, Park JH. Prediction of soil properties using vis-NIR spectroscopy combined with machine learning: a review. Sensors. 2025;25(16):5045. https://doi.org/10.3390/s25165045
  25. 25. Rossel RV, Brus D, Lobsey C, Shi Z, McLachlan G. Baseline estimates of soil organic carbon by proximal sensing: comparing design-based, model-assisted and model-based inference. Geoderma. 2016;265:152–63. https://doi.org/10.1016/j.geoderma.2015.11.016
  26. 26. Tümsavaş Z, Tekin Y, Ulusoy Y, Mouazen AM. Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy. Biosystems Engineering. 2019;177:90–100. https://doi.org/10.1016/j.biosystemseng.2018.06.008
  27. 27. Pathirana S, Lambot G, Krishnapillai M, Cheema M, Smeaton C, Galagedara L. Integrated ground-penetrating radar and electromagnetic induction offer a non-destructive approach to predict soil bulk density in boreal podzolic soil. Geoderma. 2024;450:117028. https://doi.org/10.1016/j.geoderma.2024.117028
  28. 28. Badewa E, Unc A, Cheema M, Kavanagh V, Galagedara L. Soil moisture mapping using multi-frequency and multi-coil electromagnetic induction sensors on managed podzols. Agronomy. 2018;8(10):224. https://doi.org/10.3390/agronomy8100224
  29. 29. Sharifi A, Mohsenimanesh A. Soil mechanical resistance measurement by an unique multi-cone tips horizontal sensor. International Agrophysics. 2012;26(1). https://doi.org/10.2478/v10247-012-0009-7
  30. 30. Hau N-X, Tuan N-T, Trung L-Q, Chi T-T. Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam. Chemometrics and Intelligent Laboratory Systems. 2024;255:105253. https://doi.org/10.1016/j.chemolab.2024.105253
  31. 31. Tekin Y, Kuang B, Mouazen AM. Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations. Sensors. 2013;13(8):10177–90. https://doi.org/10.3390/s130810177
  32. 32. Goff A, Huang J, Wong VNL, Monteiro Santos F, Wege R, Triantafilis J. Electromagnetic conductivity imaging of soil salinity in an estuarine-alluvial landscape. Soil Science Society of America Journal. 2014;78(5):1686–93. https://doi.org/10.2136/sssaj2014.02.0078
  33. 33. Zhao X, Wang J, Koganti T, Triantafilis J. Developing Vis-NIR libraries to predict cation exchange capacity (CEC) and pH in Australian sugarcane soil. Computers and Electronics in Agriculture. 2024;221:109004. https://doi.org/10.1016/j.compag.2024.109004
  34. 34. Wu Q, Yang Y, Xu Z, Jin Y, Guo Y, Lao C. Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil. Guang Pu Xue Yu Guang Pu Fen Xi. 2014;34(8):2102–5.
  35. 35. Seger M, Guérin R, Frison A, Bourennane H, Richard G, Cousin I. A 3D electrical resistivity tomography survey to characterise the structure of a albeluvic tonguing horizon composed of distinct elementary pedological volumes. Geoderma. 2014;219:168–76. https://doi.org/10.1016/j.geoderma.2013.12.018
  36. 36. Xue Y, Tang Z, Hu Q, Drahota J. Using the electromagnetic induction survey method to examine the depth to clay soil layer (Bt horizon) in playa wetlands. Journal of Soils and Sediments. 2020;20(1):556–70. https://doi.org/10.1007/s11368-019-02390-8
  37. 37. Narjary B, Meena MD, Kumar S, Kamra SK, Sharma DK, Triantafilis J. Digital mapping of soil salinity at various depths using an EM38. Soil Use and Management. 2019;35(2):232–44. https://doi.org/10.1111/sum.12468
  38. 38. Morse MS, Lu N, Godt JW, Revil A, Coe JA. Comparison of soil thickness in a zero-order basin in the Oregon Coast Range using a soil probe and electrical resistivity tomography. Journal of Geotechnical and Geoenvironmental Engineering. 2012;138(12):1470–82. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000717
  39. 39. Malone BP, McBratney A, Minasny B, Laslett G. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma. 2009;154(1-2):138–52. https://doi.org/10.1016/j.geoderma.2009.10.007
  40. 40. Lagacherie P, McBratney A, Voltz M. Digital soil mapping: an introductory perspective. Elsevier; 2006.
  41. 41. Bellon-Maurel V, McBratney A. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils-critical review and research perspectives. Soil Biology and Biochemistry. 2011;43(7):1398–410. https://doi.org/10.1016/j.soilbio.2011.02.019
  42. 42. Minasny B, McBratney AB. Mechanistic soil-landscape modelling as an approach to developing pedogenetic classifications. Geoderma. 2006;133(1-2):138–49. https://doi.org/10.1016/j.geoderma.2006.03.042
  43. 43. Hu Y, Cross A, Shen Z, Bouma J, Viscarra Rossel RA. On soil health and the pivotal role of sensing. Soil. 2026;12(1):227–52. https://doi.org/10.5194/soil-12-227-2026
  44. 44. Filippi P, Whelan BM, Bishop TF. Proximal and remote sensing-what makes the best farm digital soil maps? Soil Research. 2024;62(2). https://doi.org/10.1071/SR23112
  45. 45. Yang L, Li X. Digital soil mapping in the era of big data and artificial intelligence. Annals of GIS. 2026:1–25. https://doi.org/10.1080/19475683.2026.2665149
  46. 46. Radočaj D, Gašparović M, Jurišić M. Open remote sensing data in digital soil organic carbon mapping: a review. Agriculture. 2024;14(7):1005. https://doi.org/10.3390/agriculture14071005

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