This is an outdated version published on 30-06-2025. Read the
most recent version.
Review Articles
Early Access
Soil spectral libraries and their role in soil analysis
Soil and Water Department, Faculty of Agriculture, Sohag University, Sohag 82524, Egypt
Agricultural Botany Department (Genetics), Faculty of Agriculture Saba Basha, Alexandria University, Alexandria 21531, Egypt
Abstract
Soil Spectral Libraries (SSLs) play a crucial role for advancing soil testing by integrating soil spectroscopic tools. This article aimed to examines the development, utilizations and implications of the SSLs in soil science studies. The SSLs offer rapid and cost-effective quantitative estimation of different soil properties such as soil pH, salinity, nutrients, texture, organic carbon and others which is essential for environmental monitoring and precision agriculture. Furthermore, this article highlights the potentialities of the visible/near-infrared (Vis-NIR) soil spectroscopy for creating robust prediction models and demonstrating the necessity for large, variable reference datasets of the soil samples and their corresponding spectral reflectance data to enhance the applied prediction models’ accuracy. Additionally, Open Soil Spectral Library (OSSL) aims to provide an access to the soil data and engaging the external communities in the soil data collection. There are many advantages of using the SSLs, but there are some challenges especially in predicting certain soil properties accurately; and the factors related to the used prediction models and soil types. There is a necessity for creating the SSLs in India due to its importance in achieving better soil monitoring, planning and management. The prospects for SSLs are promising whereas the applicability of the machine learning models for better estimation of soil properties can be enhanced globally through collaborative efforts and increased accessibility for stakeholders in developing regions. A key limitation of this study is that the accuracy of SSLs in predicting certain soil properties can be affected by the variability in soil types and the choice of prediction models, which may limit the generalizability of the results across diverse Indian soils. Thus, this review article comprehensive overview underscores the transformative potential of SSLs in soil analysis and their critical role in sustainable land management practices.
References
- 1. Mhalla B, Ahmed N, Datta SP, Singh M, Shrivastava M, Mahapatra SK, et al. Effect of topography on characteristics: Fertility status and classification of the soils of Almora district in Uttarakhand. Journal of the Indian Society of Soil Science. 2019;67(3):309-20.
- 2. Moursy AR, Abd El-Galil A, Ibrahim MS, Abd El-Hady AA, Mhalla B. Characterization and classification of soils of Wadi Qena, Eastern Desert, Egypt. The Indian Journal of Agricultural Sciences. 2020;90(8):1544-54. https://doi.org/10.56093/ijas.v90i8.105672
- 3. Mondal BP, Sekhon BS, Banerjee K, Sharma S, Setia RK, Das B, et al. Spatial variability of soil microbiological properties under different land use systems. African Journal of Agricultural Research. 2024;20(9):825-39. https://doi.org/10.5897/AJAR2024.16720
- 4. Moursy AR, Abdelgalil AA, Ibrahim MS, Mustafa AA. Land suitability evaluation for different crops in soils of eastern Sohag, Egypt. International Journal of Academic and Applied Research. 2020;4(7):126-38. https://doi.org/10.5281/zenodo.5840397
- 5. Zhang Y, Wang J, Liu H. Integration of vis–NIR spectroscopy and machine learning techniques to predict eight soil parameters in alpine regions. Agronomy. 2023;13(11):2816. https://doi.org/10.3390/agronomy13112816
- 6. D’Amico M, De Rosa A. In situ vis-NIR spectroscopy for a basic and rapid soil investigation. Sensors. 2023;23(12):5495. https://doi.org/10.3390/s23125495
- 7. Chen Y, Zhang L, Wang Y. Exploring the potential of vis-NIR spectroscopy as a covariate in soil organic matter mapping. Remote Sensing. 2023;15(6):1617. https://doi.org/10.3390/rs15061617
- 8. Swetha RK, Mukhopadhyay S, Chakraborty S. Advancement in soil testing with new age sensors: Indian perspective. In: soil analysis. Recent Trends and Applications. 2020:55-68. https://doi.org/10.1007/978-981-15-2039-6_4
- 9. Wang L, Zhang H. Comparison of depth-specific prediction of soil properties: MIR vs. Vis-NIR spectroscopy. Sensors. 2023;23(14):37447814. https://doi.org/10.3390/s231447814
- 10. Reddy DVSC, Sahoo RN, Kondraju T, Rejith RG, Ranjan R, Bhandari A, et al. Drone-based multispectral imaging for precision monitoring of crop growth variables. in the 4th International Electronic Conference on Agronomy session Precision and Digital Agriculture.
- 11. Conforti M, Matteucci G, Buttafuoco G. Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties. Journal of Soils and Sediments. 2018;18:1009-19. https://doi.org/10.1007/s11368-017-1862-1
- 12. Piccini C, Metzger K, Debaene G, Stenberg B, Götzinger S, Borůvka L, et al. In‐field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review. European Journal of Soil Science. 2024;75(2):e13481. https://doi.org/10.1111/ejss.13481
- 13. Abd-Elazem A, El-Sayed M, Abdelsalam A, Moursy A. Soil quality and land capability evaluation for agriculture in Balat area, El Dakhla Oasis, western Desert, Egypt. Journal of the Saudi Society of Agricultural Sciences. 2024. https://doi.org/10.1016/j.jssas.2024.06.006
- 14. Wang Z, Chen S, Lu R, Zhang X, Ma Y, Shi Z. Non-linear memory-based learning for predicting soil properties using a regional vis-NIR spectral library. Geoderma. 2024;441:116752. https://doi.org/10.1016/j.geoderma.2024.116752
- 15. Cao L, Sun M, Yang Z, Jiang D, Yin D, Duan Y. A novel transformer-CNN approach for predicting soil properties from LUCAS Vis-NIR spectral data. Agronomy. 2024;14(9):1998. https://doi.org/10.3390/agronomy14091998
- 16. Safanelli JL, Hengl T, Parente LL, Minarik R, Bloom DE, Todd-Brown K, et al. Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement. PLoS One. 2025;20(1):e0296545. https://doi.org/10.1371/journal.pone.0296545
- 17. Lucena PG, Aquino RV, Sousa JE, Júnior VSS, Pacheco Filho JG, Pereira CF. Mineral and particle-size chemometric classification using handheld near-infrared instruments for soil in Northeast Brazil. Geoderma Regional. 2024;38:e00819. https://doi.org/10.1016/j.geodrs.2024.e00819
- 18. Wu M, Huang Y, Zhao X, Jin J, Ruan Y. Effects of different spectral processing methods on soil organic matter prediction based on VNIR-SWIR spectroscopy in karst areas, Southwest China. Journal of Soils and Sediments. 2024;24(2):914-27. https://doi.org/10.1007/s11368-023-03570-8
- 19. Zhao S, Ayoubi S, Mousavi SR, Mireei SA, Shahpouri F, Wu SX, et al. Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China. Journal of Environmental Management. 2024;364:121311. https://doi.org/10.1016/j.jenvman.2024.121311
- 20. Sivasakthi M, Sathiyamurthi S, Praveen Kumar S. Soil nutrient content estimation using hyperspectral remote sensing. In: soil, water pollution and mitigation strategies: A spatial approach. Cham: Springer Nature Switzerland. 2024:285-99. https://doi.org/10.1007/978-3-031-45612-1_18
- 21. Kusuma CG, Bhoomika SA, Dharumarajan S. Prediction of soil nutrients using visible-near-infrared reflectance laboratory spectroscopy. In: Remote Sensing of Soils. Elsevier. 2024:493-502. https://doi.org/10.1016/B978-0-323-91914-8.00027-2
- 22. Wilson CA, Davidson DA, Cresser MS. Multi-element soil analysis: An assessment of its potential as an aid to archaeological interpretation. Journal of Archaeological Science. 2008;35(2):412-24. https://doi.org/10.1016/j.jas.2007.04.009
- 23. Moursy AR, Ahmed N, Sahoo RN. Determination of total content of some microelements in soil using two digestion methods. International Journal of Chemical Studies. 2020;8:2510-2514. https://doi.org/10.22271/chemi.2020.v8.i2al.9127
- 24. Smith K, Mullins CE. Soil analysis. New York: Marcel Dekker. 1991.
- 25. Peverill KI. Soil analysis: An interpretation manual. CSIRO Publishing; 1999. https://doi.org/10.1071/9780643101265
- 26. Moursy A, Abdel-Aziz MA, Hassanein RAH, Abdellatif OAM, Ahmed DSM. Groundwater resources and management: A review. Eastern Journal of Agricultural and Biological Sciences. 2023;3(2):8-11.
- 27. Moursy AR, Negim OI. Quality assessment and spatial variability mapping of water sources of Sohag area, Egypt. Alexandria Journal of Soil and Water Sciences. 2022;6(2):63-78. https://doi.org/10.21608/ajsws.2022.161615.1004
- 28. Ahmed IA, Talukdar S, Baig MRI, Ramana GV, Rahman A. Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning. Remote Sensing Applications: Society and Environment. 2024;33:101088. https://doi.org/10.1016/j.rsase.2024.101088
- 29. Moursy AR, Abdelhamid OK, Abd-Elmajid JM. The potentiality of GIS for assessing soil pollution: A review.
- 30. Montgomery DR, Biklé A. Soil health and nutrient density: beyond organic vs. conventional farming. Frontiers in Sustainable Food Systems. 2021;5:699147. https://doi.org/10.3389/fsufs.2021.699147
- 31. Kumar SP, Subeesh A, Jyoti B, Mehta CR. Applications of drones in smart agriculture. In: smart agriculture for developing nations: Status, perspectives and challenges. Singapore: Springer Nature Singapore. 2023:33-48. https://doi.org/10.1007/978-981-19-9942-7_3
- 32. Negim OI, Moursy ARA. Effect of long-term irrigation with sewage wastewater on land capability of three sites in Sohag Governorate, Egypt. Journal of Soil Sciences and Agricultural Engineering. 2023;14(8):235-46.
- 33. Jia X, Cao Y, O’Connor D, Zhu J, Tsang DC, Zou B, Hou D. Mapping soil pollution by using drone image recognition and machine learning at an arsenic-contaminated agricultural field. Environmental Pollution. 2021;270:116281. https://doi.org/10.1016/j.envpol.2020.116281
- 34. Song P, Xu D, Yue J, Ma Y, Dong S, Feng J. Recent advances in soil remediation technology for heavy metal contaminated sites: A critical review. Science of the Total Environment. 2022;838:156417. https://doi.org/10.1016/j.scitotenv.2022.156417
- 35. Gupta S, Kumar D, Aziz A, AbdelRahman MA, Fiorentino C, D’Antonio P, et al. Modern optical sensing technologies and their applications in agriculture. African Journal of Agricultural Research. 2024. https://doi.org/10.5897/AJAR2024.16714
- 36. Sinha BB, Dhanalakshmi R. Recent advancements and challenges of internet of things in smart agriculture: A survey. Future Generation Computer Systems. 2022;126:169-84. https://doi.org/10.1016/j.future.2021.07.028
- 37. Thabit FN, Moursy AR. Sensors efficiency in smart management of the environmental resources. In: Handbook of nanosensors: Materials and technological applications. Cham: Springer Nature Switzerland. 2023:1-40. https://doi.org/10.1007/978-3-031-37144-8_40-1
- 38. Moursy AR, Thabit FN. Land capability and suitability evaluation of faculty of agriculture farm, Sohag, Egypt. Environment, Biodiversity and Soil Security. 2022;6:261-73. https://doi.org/10.21608/jenvbs.2022.278782
- 39. Moursy AR, El-Sheikh AO, Mahmoud BH, Abdelmageed MG. Geographic information systems for Egyptian agricultural land evaluation. International Journal of Agricultural and Applied Sciences. 2022;3(2):1-7. https://doi.org/10.52804/ijaas2022.321
- 40. Kot P, Muradov M, Gkantou M, Kamaris GS, Hashim K, Yeboah D. Recent advancements in non-destructive testing techniques for structural health monitoring. Applied Sciences. 2021;11(6):2750. https://doi.org/10.3390/app11062750
- 41. Moursy AR, Elsayed MA, Fadl ME, Abdalazem AH. PRISMA-driven hyperspectral analysis for characterization of soil salinity patterns in Sohag, Egypt. Egyptian Journal of Soil Science. 2025;65(1). https://dx.doi.org/10.21608/ejss.2024.310679.1836
- 42. Wang J, Zhen J, Hu W, Chen S, Lizaga I, Zeraatpisheh M, et al. Remote sensing of soil degradation: Progress and perspective. International Soil and Water Conservation Research. 2023;11(3):429-54. https://doi.org/10.1016/j.iswcr.2023.03.001
- 43. Dari J, Quintana-Seguí P, Escorihuela MJ, Stefan V, Brocca L, Morbidelli R. Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model. Journal of Hydrology. 2021;596:126129. https://doi.org/10.1016/j.jhydrol.2021.12612
- 44. Dubayah R, Wood EF, Lavallée D. Multiscaling analysis in distributed modeling and remote sensing: An application using soil moisture. In: Scale in remote sensing and GIS. Routledge. 2023:93-112. eBook ISBN9780203740170
- 45. Pande CB, Moharir KN. Application of hyperspectral remote sensing role in precision farming and sustainable agriculture under climate change: A review. In: Climate change impacts on natural resources. Ecosystems and Agricultural Systems. 2023:503-20. https://doi.org/10.1007/978-3-031-19059-9_21
- 46. Abd-Elazem AH, El-Sayed MA, Fadl ME, Zekari M, Selmy SA, Drosos M, et al. Estimating soil erodible fraction using multivariate regression and proximal sensing data in arid lands, South Egypt. Soil Systems. 2024;8(2):48. https://doi.org/10.3390/soilsystems8020048
- 47. 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.2015.02.001
- 48. 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
- 49. Chabrillat S, Ben-Dor E, Rossel RAV, Demattê JA. Quantitative soil spectroscopy. Applied and Environmental Soil Science. 2013:1-10. https://doi.org/10.1155/2013/616578
- 50. Trontelj J, Chambers O. Machine learning strategy for soil nutrients prediction using spectroscopic method. Sensors. 2021;21(12):4208. https://doi.org/10.3390/s21124208
- 51. Gruszczyński S, Gruszczyński W. Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response. Geoderma. 2022;405:115451. https://doi.org/10.1016/j.geoderma.2021.11545
- 52. Moursy AR. Hyperspectral remote sensing as an alternative to conventional methods of soil analysis. In: Smart technologies in sustainable agriculture. Apple Academic Press. 2025:221-54. eBook ISBN9781003493402
- 53. Veronesi F, Schillaci C. Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation. Ecological Indicators. 2019;101:1032-44. https://doi.org/10.1016/j.ecolind.2019.02.025
- 54. Lu Q, Tian S, Wei L. Digital mapping of soil pH and carbonates at the European scale using environmental variables and machine learning. Science of the Total Environment. 2023;856:159171. https://doi.org/10.1016/j.scitotenv.2022.159171
- 55. Zarei A, Hasanlou M, Mahdianpari M. A comparison of machine learning models for soil salinity estimation using multi-spectral earth observation data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2021;3:257-63. https://doi.org/10.5194/isprs-annals-V-3-2021-257-2021
- 56. Emamgholizadeh S, Bazoobandi A, Mohammadi B, Ghorbani H, Sadeghi MA. Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the caspian sea. Ain Shams Engineering Journal. 2023;14(2):101876. https://doi.org/10.1016/j.asej.2022.101876
- 57. Lotfollahi L, Delavar MA, Biswas A, Jamshidi M, Taghizadeh-Mehrjardi R. Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques. Environmental Monitoring and Assessment. 2023;195(5):607. https://doi.org/10.1007/s10661-023-11228-9
- 58. Wang Y, Yu T, Yang Z, Bo H, Lin Y, Yang Q, et al. Zinc concentration prediction in rice grain using back-propagation neural network based on soil properties and safe utilization of paddy soil: A large-scale field study in Guangxi, China. Science of the Total Environment. 2021;798:149270. https://doi.org/10.1016/j.scitotenv.2021.149270
- 59. Xu S, Wang M, Shi X, Yu Q, Zhang Z. Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles. Science of the Total Environment. 2021;754:142135. https://doi.org/10.1016/j.scitotenv.2020.142135
- 60. Kaya F, Keshavarzi A, Francaviglia R, Kaplan G, Başayiğit L, Dedeoğlu M. Assessing machine learning-based prediction under different agricultural practices for digital mapping of soil organic carbon and available phosphorus. Agriculture. 2022;12(7):1062. https://doi.org/10.3390/agriculture12071062
- 61. Diaz-Gonzalez FA, Vuelvas J, Correa CA, Vallejo VE, Patino D. Machine learning and remote sensing techniques applied to estimate soil indicators: Review. Ecological Indicators. 2022;135:108517. https://doi.org/10.1016/j.ecolind.2021.108517
- 62. Podgorski J, Araya D, Berg M. Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh–machine learning spatial prediction modeling and comparison with arsenic. Science of the Total Environment. 2022;833:155131. https://doi.org/10.1016/j.scitotenv.2022.155131
- 63. Zhou M, Hu T, Wu M, Ma C, Qi C. Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale. Ecological Informatics. 2024;81:102615. https://doi.org/10.1016/j.ecoinf.2024.102615
- 64. Yang H, Huang K, Zhang K, Weng Q, Zhang H, Wang F. Predicting heavy metal adsorption on soil with machine learning and mapping global distribution of soil adsorption capacities. Environmental Science & Technology. 2021;55(20):14316-28. https://doi.org/10.1021/acs.est.1c04058
- 65. Liao Q, Gu H, Qi C, Chao J, Zuo W, Liu J, et al. Mapping global distributions of clay-size minerals via soil properties and machine learning techniques. Science of the Total Environment. 2024;949:174776. https://doi.org/10.1016/j.scitotenv.2024.174776
- 66. Andrychowicz M, Espeholt L, Li D, Merchant S, Merose A, Zyda F, et al. Deep learning for day forecasts from sparse observations. arXiv preprint. 2023;arXiv:2306.06079. https://doi.org/10.48550/arXiv.2306.06079
- 67. Wong PY, Lee HY, Chen YC, Zeng YT, Chern YR, Chen NT, et al. Using a land use regression model with machine learning to estimate ground level PM2.5. Environmental Pollution. 2021;277:116846. https://doi.org/10.1016/j.envpol.2021.116846
- 68. Baumann P, Helfenstein A, Gubler A, Keller A, Meuli RG, Wächter D, et al. Developing the Swiss soil spectral library for local estimation and monitoring. Soil Discussions. 2021;2021:1-32. https://doi.org/10.5194/soil-2021-61
- 69. Zhong L, Guo X, Xu Z, Ding M. Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks. Geoderma. 2021;402:115366. https://doi.org/10.1016/j.geoderma.2021.115366
- 70. Breure TS, Prout JM, Haefele SM, Milne AE, Hannam JA, Moreno-Rojas S, et al. Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near-and mid-infrared spectra at the field-scale. Soil and Tillage Research. 2022;215:105196. https://doi.org/10.1016/j.still.2021.105196
- 71. Ng W, Minasny B, Jones E, McBratney A. To spike or to localize? Strategies to improve the prediction of local soil properties using regional spectral library. Geoderma. 2022;406:115501. https://doi.org/10.1016/j.geoderma.2021.115501
- 72. Shepherd KD, Ferguson R, Hoover D, van Egmond F, Sanderman J, Ge Y. A global soil spectral calibration library and estimation service. Soil Security. 2022;7:100061. https://doi.org/10.1016/j.soisec.2022.100061
- 73. Hengl T, Miller MA, Križan J, Shepherd KD, Sila A, Kilibarda M, et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports. 2021;11(1):6130. https://doi.org/10.1038/s41598-021-85639-y
- 74. Demattê JA, Paiva AFDS, Poppiel RR, Rosin NA, Ruiz LFC, Mello FAO, et al. The Brazilian soil spectral service (BraSpecS): A user-friendly system for global soil spectra communication. Remote Sensing. 2022;14(3):740. https://doi.org/10.3390/rs14030740
- 75. Moloney JP, Malone BP, Karunaratne S, Stockmann U. Leveraging large soil spectral libraries for sensor-agnostic field condition predictions of several agronomically important soil properties. Geoderma. 2023;439:116651. https://doi.org/10.1016/j.geoderma.2023.116651
- 76. Yang M, Chen S, Xu D, Hong Y, Li S, Peng J, et al. Strategies for predicting soil organic matter in the field using the Chinese Vis-NIR soil spectral library. Geoderma. 2023;433:116461. https://doi.org/10.1016/j.geoderma.2023.116461
- 77. Dharumarajan S, Gomez C, Lalitha M, Kalaiselvi B, Vasundhara R, Hegde R. Soil order knowledge as a driver in soil properties estimation from Vis-NIR spectral data: Case study from northern Karnataka (India). Geoderma Regional. 2023;32:e00596. https://doi.org/10.1016/j.geodrs.2022.e00596
- 78. Moursy AR, Hassan MN, Elhefny TM. Sampling and analysis of soil and water: A review. International Journal of Geography, Geology and Environment. 2022;4:34-41.
- 79. Pande CB, Kadam SA, Jayaraman R, Gorantiwar S, Shinde M. Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods. Journal of the Saudi Society of Agricultural Sciences. 2022;21(1):21-8. https://doi.org/10.1016/j.jssas.2020.11.003
- 80. Fadl ME, Moursy AR, Abdel-Azem AH, El-Sayed MA. A geospatial approach to land capability assessment in arid regions: Integration of Storie Index, geographic information systems and analytical hierarchy process techniques. Journal of Arid Environments. 2025;229:105373. https://doi.org/10.1016/j.jaridenv.2023.105373
- 81. Selmy SA, Kucher DE, Moursy AR. Integrating remote sensing, GIS and AI technologies in soil erosion studies. In: Soil Erosion Studies. IntechOpen. 2025. https://doi.org/10.5772/intechopen.1008677
- 82. Selmy SA, Moursy AR. Geospatial issues (remote sensing and GIS) of the wetlands. In: wetland chemistry. Jenny Stanford Publishing. 2025:27-57. eBook ISBN9781003616092
- 83. Moursy AR, Thabit FN. Wetlands: Climate change impacts and strategies. In: wetland chemistry. Jenny Stanford Publishing; 2025:77-100. eBook ISBN9781003616092
- 84. Moursy AR. Hyperspectral remote sensing as an alternative to conventional methods of soil analysis. In: Smart technologies in sustainable agriculture. Apple Academic Press. 2025:221-54. eBook ISBN9781003493402
Downloads
Download data is not yet available.