The state-of-the-art use of X-Ray and Infrared for evaluating soil attributes


  • João Carlos Arruda-Oliveira
  • Mayco Mascarello Richardi
  • Wagner Arruda de Jesus
  • Emerson Silva Miranda
  • Daniela Tiago da Silva Campos
  • Diego Pierotti Procópio
  • Oscarlina Lúcia dos Santos Weber



Sustainable Agriculture, Soil Analysis, Instrumental Methods


Ensuring food supply to society is crucial. Therefore, understanding the specificities of soils and climates in different countries becomes indispensable. Assessing and quantifying soil attributes play a fundamental role in the sustainable management of natural resources, promoting increased crop productivity and soil and biodiversity resilience. This review aims to analyze the main instrumental methods (NIRS, XRF, XRD, and FTIR), exploring their mode of action, challenges in implementing these methods, and emerging trends for sustainable soil management. Scientific articles indexed in two databases, Web of Science and Scopus, were searched using the keywords "soil attributes" and NIRS or "near-infrared spectroscopy" or XRF or "X-ray fluorescence spectrometry" or DRX or "X-ray diffraction" or FTIR or "Fourier-transform infrared spectroscopy", without restrictions in the field of research. Instrumental methods, when properly calibrated, provide rapid, accurate, and non-destructive information crucial for making agricultural decisions and sustainable soil management.  However, challenges such as high equipment costs, complexity of analyses, and calibration dependence are faced. Nevertheless, current trends indicate promising prospects, with the pursuit of technological innovations and the integration of artificial intelligence and machine learning to simplify complex data analysis and mitigate the challenges faced. Therefore, we conclude that the instrumental methods discussed, such as NIRS, XRF, DRX, and FTIR, stand out as an indispensable set of tools in the analysis of soil particle size, chemical, and mineralogical properties, emerging as an innovative and essential response to contemporary challenges related to food security, environmental sustainability, and efficient management of natural resources.


AGUIAR, M.I. DE; RIBEIRO, L.P.D.; RAMOS, A.P. DOS; CARDOSO, E.L. Soil characterization by near-infrared spectroscopy and principal component analysis. REVISTA CIÊNCIA AGRONÔMICA, p.2021, 2021. DOI: 10.5935/1806-6690.20210004. DOI:

ANDRADE, R.; MANCINI, M.; TEIXEIRA, A.F. DOS S.; SILVA, S.H.G.; WEIN-DORF, D.C.; CHAKRABORTY, S.; GUILHERME, L.R.G.; CURI, N. Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture. Geoderma, v.422, p.115936, 2022. DOI: 10.1016/j.geoderma.2022.115936. DOI:

BAHIA, A.S.R. DE S.; MARQUES, J.; SCALA, N. LA; PELLEGRINO CERRI, C.E.; CAMARGO, L.A. Prediction and Mapping of Soil Attributes using Diffuse Reflectance Spectroscopy and Magnetic Susceptibility. Soil Science Society of America Journal, v.81, p.1450–1462, 2017. DOI: 10.2136/sssaj2017.06.0206. DOI:

BAHIA, A.S.R. DE S.; MARQUES, J.; SIQUEIRA, D.S. Procedures using dif-fuse reflectance spectroscopy for estimating hematite and goethite in Oxisols of São Paulo, Brazil. Geoderma Regional, v.5, p.150–156, 2015. DOI: 10.1016/j.geodrs.2015.04.006. DOI:

BENEDET, L.; FARIA, W.M.; SILVA, S.H.G.; MANCINI, M.; DEMATTÊ, J.A.M.; GUILHERME, L.R.G.; CURI, N. Soil texture prediction using portable X-ray fluo-rescence spectrometry and visible near-infrared diffuse reflectance spectros-copy. Geoderma, v.376, p.114553, 2020. DOI: 10.1016/j.geoderma.2020.114553. DOI:

BENEDET, L.; SILVA, S.H.G.; MANCINI, M.; TEIXEIRA, A.F. DOS S.; INDA, A.V.; DEMATTÊ, J.A.M.; CURI, N. Variation of properties of two contrasting Oxisols enhanced by pXRF and Vis-NIR. Journal of South American Earth Sciences, v.115, p.103748, 2022. DOI: 10.1016/j.jsames.2022.103748. DOI:

CAMPBELL, P.M. DA M.; FRANCELINO, M.R.; FERNANDES FILHO, E.I.; RO-CHA, P. DE A.; AZEVEDO, B.C. DE. Digital mapping of soil attributes using machine learning. REVISTA CIÊNCIA AGRONÔMICA, v.50, 2019. DOI: 10.5935/1806-6690.20190061. DOI:

CANAL FILHO, R.; MOLIN, J.P.; WEI, M.C.F.; SILVA, E.R.O. DA. Soil Attributes Mapping with Online Near-Infrared Spectroscopy Requires Spatio-Temporal Local Calibrations. AgriEngineering, v.5, p.1163–1177, 2023. DOI: 10.3390/agriengineering5030074. DOI:

CHRISTY, C.D. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture, v.61, p.10–19, 2008. DOI: 10.1016/j.compag.2007.02.010. DOI:

FERNANDES, K.; MARQUES JÚNIOR, J.; BAHIA, A.S.R. DE S.; DEMATTÊ, J.A.M.; RIBON, A.A. Landscape-scale spatial variability of kaolinite-gibbsite ra-tio in tropical soils detected by diffuse reflectance spectroscopy. CATENA, v.195, p.104795, 2020. DOI: 10.1016/j.catena.2020.104795. DOI:

FERREIRA, T.O.; OTERO, X.L.; SOUZA JUNIOR, V.S.; VIDAL-TORRADO, P.; MACÍAS, F.; FIRME, L.P. Spatial patterns of soil attributes and components in a mangrove system in Southeast Brazil (São Paulo). Journal of Soils and Sed-iments, v.10, p.995–1006, 2010. DOI: 10.1007/s11368-010-0224-4. DOI:

FONSECA, J. DA S.; CAMPOS, M.C.C.; BRITO FILHO, E.G. DE; MANTO-VANELLI, B.C.; SILVA, L.S.; LIMA, A.F.L.; CUNHA, J.M.; SIMÕES, E.L.; SAN-TOS, L.A.C. Soil–landscape relationship in a sandstone-gneiss topolithose-quence in the State of Amazonas, Brazil. Environmental Earth Sciences, v.80, p.1–15, 2021. DOI: 10.1007/S12665-021-10026-9/FIGURES/5. DOI:

FONTENELLI, J. V.; ADAMCHUK, V.I.; FERREIRA, M.M.C.; AMARAL, L.R.; GUIMARÃES, C.C.B.; DEMATTÊ, J.A.M.; MAGALHÃES, P.S.G. Evaluating the synergy of three soil spectrometers for improving the prediction and mapping of soil properties in a high anthropic management area: A case of study from Southeast Brazil. Geoderma, v.402, p.115347, 2021. DOI: 10.1016/j.geoderma.2021.115347. DOI:

FUENTES-LLANILLO, R.; TELLES, T.S.; SOARES JUNIOR, D.; MELO, T.R. DE; FRIEDRICH, T.; KASSAM, A. Expansion of no-tillage practice in conserva-tion agriculture in Brazil. Soil and Tillage Research, v.208, p.104877, 2021. DOI: 10.1016/j.still.2020.104877. DOI:

GRUNWALD, S.; YU, C.; XIONG, X. Transferability and Scalability of Soil Total Carbon Prediction Models in Florida, USA. Pedosphere, v.28, p.856–872, 2018. DOI: 10.1016/S1002-0160(18)60048-7. DOI:

GUIMARÃES, C.C.B.; DEMATTÊ, J.A.M.; AZEVEDO, A.C. DE; DALMOLIN, R.S.D.; CATEN, A. TEN; SAYÃO, V.M.; SILVA, R.C. DA; POPPIEL, R.R.; MENDES, W. DE S.; SALAZAR, D.F.U.; SOUZA, A.B. E. Soil weathering be-havior assessed by combined spectral ranges: Insights into aggregate analysis. Geoderma, v.402, p.115154, 2021. DOI: 10.1016/j.geoderma.2021.115154. DOI:

JAVADI, S.H.; MOUAZEN, A.M. Data Fusion of XRF and Vis-NIR Using Outer Product Analysis, Granger–Ramanathan, and Least Squares for Prediction of Key Soil Attributes. Remote Sensing, v.13, p.2023, 2021. DOI: 10.3390/rs13112023. DOI:

JAVADI, S.H.; MUNNAF, M.A.; MOUAZEN, A.M. Fusion of Vis-NIR and XRF spectra for estimation of key soil attributes. Geoderma, v.385, p.114851, 2021. DOI: 10.1016/j.geoderma.2020.114851. DOI:

JI, W.; LI, S.; CHEN, S.; SHI, Z.; VISCARRA ROSSEL, R.A.; MOUAZEN, A.M. Prediction of soil attributes using the Chinese soil spectral library and stand-ardized spectra recorded at field conditions. Soil and Tillage Research, v.155, p.492–500, 2016. DOI: 10.1016/j.still.2015.06.004. DOI:

JOZANIKOHAN, G.; ABARGHOOEI, M.N. The Fourier transform infrared spec-troscopy (FTIR) analysis for the clay mineralogy studies in a clastic reservoir. Journal of Petroleum Exploration and Production Technology, v.12, p.2093–2106, 2022. DOI: 10.1007/s13202-021-01449-y. DOI:

KANDPAL, L.M.; MUNNAF, M.A.; CRUZ, C.; MOUAZEN, A.M. Spectra Fusion of Mid-Infrared (MIR) and X-ray Fluorescence (XRF) Spectroscopy for Estima-tion of Selected Soil Fertility Attributes. Sensors, v.22, p.3459, 2022. DOI: 10.3390/s22093459. DOI:

LI, R.; YIN, B.; CONG, Y.; DU, Z. Simultaneous Prediction of Soil Properties Using Multi_CNN Model. Sensors, v.20, p.6271, 2020. DOI: 10.3390/s20216271. DOI:

LOPES, T.C. DE S.; PORTELA, J.C.; BATISTA, R.O.; BANDEIRA, D.J. DA C.; LEITE, I. DE O.; RAMALHO, L.B.; GONDIM, J.E.F.; COSTA, J.D. DA; GURGEL, M.T.; SOUZA, C.M.M.; SILVA, E.F. DA; SOUZA, E.R. DE; OLIVEIRA, F.H.T. DE; MIRANDA, N. DE O.; SÁ, F.V. DA S. Clay Fraction Mineralogy and Structural Soil Attributes of Two Soil Classes under the Semi-Arid Climate of Brazil. Land, v.11, p.2192, 2022. DOI: 10.3390/land11122192. DOI:

LOTFOLLAHI, L.; DELAVAR, M.A.; BISWAS, A.; FATEHI, S.; SCHOLTEN, T. Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy. Journal of Environmental Management, v.345, p.118854, 2023. DOI: 10.1016/j.jenvman.2023.118854. DOI:

LU, P.; WANG, L.; NIU, Z.; LI, L.; ZHANG, W. Prediction of soil properties using laboratory VIS–NIR spectroscopy and Hyperion imagery. Journal of Geochem-ical Exploration, v.132, p.26–33, 2013. DOI: 10.1016/j.gexplo.2013.04.003. DOI:

MA, F.; ZENG, Y.; DU, C.; SHEN, Y.; MA, H.; XU, S.; ZHOU, J. Soil variability description using Fourier transform mid-infrared photoacoustic spectroscopy coupling with RGB method. CATENA, v.152, p.190–197, 2017. DOI: 10.1016/j.catena.2017.01.005. DOI:

MA, Y.; ROUDIER, P.; KUMAR, K.; PALMADA, T.; GREALISH, G.; CARRICK, S.; LILBURNE, L.; TRIANTAFILIS, J. A soil spectral library of New Zealand. Geoderma Regional, v.35, p.e00726, 2023. DOI: 10.1016/j.geodrs.2023.e00726. DOI:

MOHAMMEDZEİN, M.A.; CSORBA, A.; ROTİCH, B.; JUSTİN, P.N.; MELENYA, C.; ANDREİ, Y.; MİCHELİ, E. Development of Hungarian spectral library: Pre-diction of soil properties and applications. Eurasian Journal of Soil Science (EJSS), v.12, p.244–256, 2023. DOI: 10.18393/ejss.1275149. DOI:

NAWAR, S.; MUNNAF, M.A.; MOUAZEN, A.M. Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect. Remote Sensing, v.12, p.1308, 2020. DOI: 10.3390/rs12081308. DOI:

NG, W.; MINASNY, B.; MENDES, W. DE S.; DEMATTÊ, J.A.M. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. SOIL, v.6, p.565–578, 2020. DOI: 10.5194/soil-6-565-2020. DOI:

NOVAIS, J.J.; POPPIEL, R.R.; LACERDA, M.P.C.; DEMATTÊ, J.A.M. VNIR-SWIR Spectroscopy, XRD and Traditional Analyses for Pedomorphogeological Assessment in a Tropical Toposequence. AgriEngineering, v.5, p.1581–1598, 2023. DOI: 10.3390/agriengineering5030098. DOI:

OLIVEIRA, I.R.; GONTIJO NETO, M.M.; NOBRE, M.M. Mudanças climáticas e a agricultura de baixa emissão de carbono. In: NOBRE, M.M.; OLIVEIRA, I.R. (Ed.). Agricultura de baixo carbono: tecnologias e estratégias de implanta-ção. Brasília: Embrapa, 2018. p.10–32.

OLIVEIRA JR., J.C.; FREITAS MELO, V.; SOUZA, L.C.P.; ROCHA, H.O. Terrain attributes and spatial distribution of soil mineralogical attributes. Geoderma, v.213, p.214–225, 2014. DOI: 10.1016/j.geoderma.2013.08.020. DOI:

POPP, J.; PETŐ, K.; NAGY, J. Pesticide productivity and food security. A re-view. Agronomy for Sustainable Development, v.33, p.243–255, 2013. DOI: 10.1007/S13593-012-0105-X/FIGURES/4. DOI:

REDA, R.; SAFFAJ, T.; DERROUZ, H.; ITQIQ, S.E.; BOUZIDA, I.; SAIDI, O.; LAKSSIR, B.; HADRAMI, E.M. EL. Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan ag-ricultural regions using NIR spectroscopy. Chemometrics and Intelligent La-boratory Systems, v.211, p.104277, 2021. DOI: 10.1016/j.chemolab.2021.104277. DOI:

REDA, R.; SAFFAJ, T.; ITQIQ, S.E.; BOUZIDA, I.; SAIDI, O.; YAAKOUBI, K.; LAKSSIR, B.; MERNISSI, N. EL; HADRAMI, E.M. EL. Predicting soil phospho-rus and studying the effect of texture on the prediction accuracy using ma-chine learning combined with near-infrared spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, v.242, p.118736, 2020. DOI: 10.1016/j.saa.2020.118736. DOI:

REETZ, H.F. Fertilizantes e o seu uso eficiente. São Paulo: ANDA, 2017.

SANTOS, F.R.; OLIVEIRA, J.F.; BONA, E.; BARBOSA, G.M.C.; MELQUIADES, F.L. Data fusion of XRF and vis-NIR using p-ComDim to predict some fertility attributes in tropical soils derived from basalt. Microchemical Journal, v.191, p.108813, 2023. DOI: 10.1016/j.microc.2023.108813. DOI:

SHEN, Z.-Q.; SHAN, Y.-J.; PENG, L.; JIANG, Y.-G. Mapping of Total Carbon and Clay Contents in Glacial Till Soil Using On-the-Go Near-Infrared Reflec-tance Spectroscopy and Partial Least Squares Regression. Pedosphere, v.23, p.305–311, 2013. DOI: 10.1016/S1002-0160(13)60020-X. DOI:

SOUZA, A.B. E; RAIMO, L.A.D.L. DI; MELLO, D.C. DE; GUIMARÃES, C.C.B.; URBINA-SALAZAR, D.F.; SILVA, S.H.G.; CURI, N.; DEMATTÊ, J.A.M. Surface reflectance and pXRF for assessing soil weathering indexes. Journal of South American Earth Sciences, v.115, p.103747, 2022. DOI: 10.1016/j.jsames.2022.103747. DOI:

TAVARES, T.R.; MOLIN, J.P.; JAVADI, S.H.; CARVALHO, H.W.P. DE; MOUA-ZEN, A.M. Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertili-ty Analysis: Assessing Different Data Fusion Approaches. Sensors, v.21, p.148, 2020. DOI: 10.3390/s21010148. DOI:

WANG, Y.; HUANG, T.; LIU, J.; LIN, Z.; LI, S.; WANG, R.; GE, Y. Soil pH value, organic matter and macronutrients contents prediction using optical diffuse reflectance spectroscopy. Computers and Electronics in Agriculture, v.111, p.69–77, 2015. DOI: 10.1016/j.compag.2014.11.019. DOI:

WEI, M.C.F.; CANAL FILHO, R.; TAVARES, T.R.; MOLIN, J.P.; VIEIRA, A.M.C. Dimensionality Reduction Statistical Models for Soil Attribute Prediction Based on Raw Spectral Data. AI, v.3, p.809–819, 2022. DOI: 10.3390/ai3040049. DOI:




How to Cite

Arruda-Oliveira, J. C., Richardi , M. M., Jesus, W. A. de, Miranda , E. S., Campos, D. T. da S., Procópio , D. P., & Weber , O. L. dos S. (2024). The state-of-the-art use of X-Ray and Infrared for evaluating soil attributes. Caderno Pedagógico, 21(3), e3380.