Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Danilo César de Mello
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Gustavo Vieira Veloso
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Marcos Guedes de Lana
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Fellipe Alcantara de Oliveira Mello
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Raul Roberto Poppiel
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Diego Ribeiro Oquendo Cabrero
Geography Department of Federal University of Mato Grosso do Sul, Av. Ranulpho Marques Leal, no. 3484, Distrito Industrial CEP
79610-100 Três Lagoas/MS, Brazil
Luis Augusto Di Loreto Di Raimo
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
Carlos Ernesto Gonçalves Reynaud Schaefer
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Elpídio Inácio Fernandes Filho
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Emilson Pereira Leite
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
José Alexandre Melo Demattê
CORRESPONDING AUTHOR
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Related authors
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-2, https://doi.org/10.5194/gmd-2024-2, 2024
Preprint under review for GMD
Short summary
Short summary
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
Danilo César de Mello, Tiago Osório Ferreira, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Luis Augusto Di Loreto Di Raimo, Diego Ribeiro Oquendo Cabrero, José João Lelis Leal de Souza, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, and José A. M. Demattê
SOIL Discuss., https://doi.org/10.5194/soil-2022-17, https://doi.org/10.5194/soil-2022-17, 2022
Revised manuscript not accepted
Short summary
Short summary
We proposed a different method to evaluate different intensities of weathering in a heterogeneous area (soils, geology and relief) and small number of samples. We use combined data from three geophysical sensors, clustering and machine learning (nested-leave-one-out-cross-validation) to distinguish weathering intensities and assess the relationship of these variables with weathering, relief, geology, and soil types and attributes. and we obtained satisfactory performances of models evaluation.
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-2, https://doi.org/10.5194/gmd-2024-2, 2024
Preprint under review for GMD
Short summary
Short summary
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
Danilo César de Mello, Tiago Osório Ferreira, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Luis Augusto Di Loreto Di Raimo, Diego Ribeiro Oquendo Cabrero, José João Lelis Leal de Souza, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, and José A. M. Demattê
SOIL Discuss., https://doi.org/10.5194/soil-2022-17, https://doi.org/10.5194/soil-2022-17, 2022
Revised manuscript not accepted
Short summary
Short summary
We proposed a different method to evaluate different intensities of weathering in a heterogeneous area (soils, geology and relief) and small number of samples. We use combined data from three geophysical sensors, clustering and machine learning (nested-leave-one-out-cross-validation) to distinguish weathering intensities and assess the relationship of these variables with weathering, relief, geology, and soil types and attributes. and we obtained satisfactory performances of models evaluation.
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê
SOIL, 6, 565–578, https://doi.org/10.5194/soil-6-565-2020, https://doi.org/10.5194/soil-6-565-2020, 2020
Short summary
Short summary
The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
Cited articles
Agbu, P. A., Fehrenbacher, D. J., and Jansen, I. J.: Soil property
relationships with SPOT satellite digital data in east central Illinois,
Soil Sci. Soc. Am. J., 54, 807–812, 1990.
Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes Gonçalves, J.
L., and Sparovek, G.: Köppen's climate classification map for Brazil,
Meteorol. Z., 22, 711–728, https://doi.org/10.1127/0941-2948/2013/0507,
2013.
Amundson, R., Berhe, A. A., Hopmans, J. W., Olson, C., Sztein, A. E., and
Sparks, D. L.: Soil and human security in the 21st century, Science, 348, 6235, https://doi.org/10.1126/science.1261071, 2015.
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G.
B. M., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie,
N. J., Mendonca-Santos, M. d. L., Minasny, B., Montanarella, L., Odeh, I. O.
A., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Toward
a Fine-Resolution Global Grid of Soil Properties, Adv. Agron.,
125, 93–134, 2014.
Ayoubi, S., Abazari, P., and Zeraatpisheh, M.: Soil great groups
discrimination using magnetic susceptibility technique in a semi-arid
region, central Iran, Arab. J. Geosci., 11, 1–12,
https://doi.org/10.1007/s12517-018-3941-4, 2018.
Bai, W., Kong, L., and Guo, A.: Effects of physical properties on electrical
conductivity of compacted lateritic soil, J. Rock Mech. Geotech. Eng., 5,
406–411, https://doi.org/10.1016/j.jrmge.2013.07.003, 2013.
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, https://doi.org/10.1016/j.geoderma.2015.07.006, 2016.
Barbuena, D., de Souza Filho, C. R., Leite, E. P., Miguel, E., de Assis,
R. R., Xavier, R. P., Ferreira, F. J. F., and Paes de Barros, A. J.: Airborne
geophysical data analysis applied to geological interpretation in the Alta
Floresta Gold Province, MT, Rev. Bras. Geofis., 31, 169–186, 2013.
Batty, M. and Torrens, P. M.: Modelling complexity: the limits to
prediction, Cybergeo Eur. J. Geogr., https://doi.org/10.4000/cybergeo.1035, 2001.
Bauer, F. C.: Water flow paths in soils of an undisturbed and landslide
affected mature montane rainforest in South Ecuador, PhD thesis, University of Bayreuth, Germany, available at: https://epub.uni-bayreuth.de/395/ (last access: 2 February 2022), 2010.
Bazaglia Filho, O., Rizzo, R., Lepsch, I. F., Prado, H. D., Gomes, F. H., Mazza, J. A., and Demattê, J. A. M.: Comparação entre mapas de solos detalhados obtidos pelos métodos convencional e digital em uma área de geologia complexa, Rev. Bras. Cienc. Solo, 37, 1136–1148, available at: https://www.scielo.br/j/rbcs/a/cbGQmJwJ3LqpznTnM5zktXf/?format=pdf&lang=en, 2013.
Bazaglia Filho, O., Rizzo, R., Lepsch, I. F., do Prado, H., Gomes, F. H.,
Mazza, J. A., and Demattê, J. A. M.: Comparison between detailed digital
and conventional soil maps of an area with complex geology, Rev. Bras.
Cienc. Solo, 37, 1136–1148, https://doi.org/10.1590/s0100-06832013000500003,
2013.
Beamish, D.: Gamma ray attenuation in the soils of Northern Ireland, with
special reference to peat, J. Environ. Radioactiv., 115, 13–27,
https://doi.org/10.1016/j.jenvrad.2012.05.031, 2013.
Beamish, D.: Relationships between gamma-ray attenuation and soils in SW
England, Geoderma, 259–260, 174–186, https://doi.org/10.1016/j.geoderma.2015.05.018,
2015.
Beckett, P. H. T.: Soil variability: a review, Soils Fertil., 34, 1–15,
1971.
Bigham, J. M., Fitzpatrick, R. W., and Schulze, D. G.: Iron oxides, in: Soil mineralogy with environmental applications, 7th edn., edited by: Dixon, J. B. and Schulze, D. G., SSSA Book Series, 323–366, https://doi.org/10.2136/sssabookser7.c10, 2002.
Blundell, A., Dearing, J. A., Boyle, J. F., and Hannam, J. A.: Controlling
factors for the spatial variability of soil magnetic susceptibility across
England and Wales, Earth-Sci. Rev., 95, 158–188,
https://doi.org/10.1016/j.earscirev.2009.05.001, 2009.
Bockheim, J. G., Gennadiyev, A. N., Hartemink, A. E., and Brevik, E. C.: Soil-forming factors and Soil Taxonomy, Geoderma, 226, 231–237, 2014.
Breemen, N. and Buurman, P.: Soil Formation, 2nd edn., Laboratory of Soil
Science and Geology, Kluwer, New York, Boston, Dordrecht, London, Moscow, https://doi.org/10.1017/CBO9781107415324.004, 2003.
Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., and Edwards Jr., T. C.: Machine learning for predicting soil classes in three semi-arid landscapes, Geoderma, 239, 68–83, 2015.
Camargo, L. A., Marques Júnior, J., Pereira, G. T., and Bahia de Souza, A. S. R.: Clay mineralogy and magnetic susceptibility of Oxisols in geomorphic
surfaces, Sci. Agric., 71, 244–256, https://doi.org/10.1590/S0103-90162014000300010,
2014.
Camargo, O. A., Moniz, A. C., Jorge, J. A., and Valadares, J. M. A. S.:
Métodos de análise química, mineralógica e física de
solos do Instituto Agronômico do estado de São Paulo, Bol.
téc., 106, 94, 1986.
Cardoso, R. and Dias, A. S.: Study of the electrical resistivity of
compacted kaolin based on water potential, Eng. Geol., 226, 1–11, https://doi.org/10.1016/j.enggeo.2017.04.007, 2017.
Clevers, J. G. P. W., Van Der Heijden, G. W. A. M., Verzakov, S., and
Schaepman, M. E.: Estimating grassland biomass using SVM band shaving of
hyperspectral data, Photogramm. Eng. Rem. S., 73, 1141–1148,
https://doi.org/10.14358/PERS.73.10.1141, 2007.
Coblinski, J. A., Inda, A. V., Demattê, J. A. M., Dotto, A. C.,
Gholizadeh, A., and Giasson, É.: Identification of minerals in
subtropical soils with different textural classes by VIS–NIR–SWIR
reflectance spectroscopy, Catena, 203, 105334, https://doi.org/10.1016/j.catena.2021.105334, 2021.
Correia, M. G., Leite, E. P., and de Souza Filho, C. R.: Comparação
de métodos de estimativa de profundidades de fontes magnéticas
utilizando dados aeromagnéticos da província mineral de
Carajás, Pará, Braz. J. Geophys., 28, 411–426, 2010.
Corwin, D. L., Lesch, S. M., Shouse, P. J., Soppe, R., and Ayars, J. E.:
Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling
Directed by Apparent Soil Electrical Conductivity, 95, 352–364, 2003.
Cracknell, M. J. and Reading, A. M.: The upside of uncertainty:
Identification of lithology contact zones from airborne geophysics and
satellite data using random forests and support vector machines, Geophysics,
78, WB113–WB126, https://doi.org/10.1190/GEO2012-0411.1, 2013.
Da Costa, A. C. S., Bigham, J. M., Rhoton, F. E., and Traina, S. J.:
Quantification and characterization of maghemite in soils derived from
volcanic rocks in southern Brazil, Clay. Clay Miner., 47, 466–473,
https://doi.org/10.1346/CCMN.1999.0470408, 1999.
Damaceno, J. G., de Castro, D. L., Valcácio, S. N., and Souza, Z. S.:
Magnetic and gravity modeling of a Paleogene diabase plug in Northeast
Brazil, J. Appl. Geophys., 136, 219–230, https://doi.org/10.1016/j.jappgeo.2016.11.006,
2017.
Darst, B. F., Malecki, K. C., and Engelman, C. D.: Using recursive feature
elimination in random forest to account for correlated variables in high
dimensional data, BMC Genet., 19, 65, https://doi.org/10.1186/s12863-018-0633-8, 2018.
De Jong, E., Pennock, D. J., and Nestor, P. A.: Magnetic susceptibility of
soils in different slope positions in Saskatchewan, Canada, Catena, 40,
291–305, https://doi.org/10.1016/S0341-8162(00)00080-1, 2000.
de Mello, D. C., Demattê, J. A., Silvero, N. E., Di Raimo, L. A., Poppiel, R. R., Mello, F. A., Souza, A. B., Safanelli, J.
L., Resende, M. E. B., and Rizzo, R.: Soil magnetic susceptibility and its
relationship with naturally occurring processes and soil attributes in
pedosphere, in a tropical environment, Geoderma, 372, 114364,
https://doi.org/10.1016/j.geoderma.2020.114364, 2020.
De Souza Bahia, A. S. R., Marques, J., La Scala, N., Pellegrino Cerri, C. E.,
and Camargo, L. A.: Prediction and mapping of soil attributes using diffuse
reflectance spectroscopy and magnetic susceptibility, Soil Sci. Soc. Am. J.,
81, 1450–1462, 2017.
Demattê, J. A. M., Galdos, M. V, Guimarães, R. V, Genú, A. M.,
Nanni, M. R., and Zullo, J.: Quantification of tropical soil attributes
from ETM+/LANDSAT-7 data, Int. J. Remote Sens., 28, 3813–3829, 2007.
Demattê, J. A. M., Horák-Terra, I., Beirigo, R. M., da Silva Terra, F., Marques, K. P. P., Fongaro, C. T., Silva, A. C., and Vidal-Torrado, P.:
Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for
environmental monitoring, J. Environ. Manage., 197, 50–62,
https://doi.org/10.1016/j.jenvman.2017.03.014, 2017.
Demattê, J. A. M., Dotto, A. C., Paiva, A. F. S., Sato, M. V., Dalmolin,
R. S. D., do Socorro B. de Araújo, M., da Silva, E. B., Nanni, M. R., ten
Caten, A., Noronha, N. C., Lacerda, M. P. C., de Araújo Filho, J. C.,
Rizzo, R., Bellinaso, H., Francelino, M. R., Schaefer, C. E. G. R., Vicente,
L. E., dos Santos, U. J., de Sá Barretto Sampaio, E. V., Menezes, R. S.
C., de Souza, J. J. L. L., Abrahão, W. A. P., Coelho, R. M., Grego, C.
R., Lani, J. L., Fernandes, A. R., Gonçalves, D. A. M., Silva, S. H. G.,
de Menezes, M. D., Curi, N., Couto, E. G., dos Anjos, L. H. C., Ceddia, M.
B., Pinheiro, É. F. M., Grunwald, S., Vasques, G. M., Marques
Júnior, J., da Silva, A. J., de Vasconcelos Barreto, M. C., Nóbrega, G. N., da Silva, M. Z., de Souza, S. F., Valladares, G. S., Viana, J. H. M., da
Silva Terra, F., Horák-Terra, I., Fiorio, P. R., da Silva, R. C., Frade
Júnior, E. F., Lima, R. H. C., Alba, J. M. F., de Souza Junior, V. S.,
Brefin, M. D. L. M. S., Ruivo, M. D. L. P., Ferreira, T. O., Brait, M. A.,
Caetano, N. R., Bringhenti, I., de Sousa Mendes, W., Safanelli, J. L.,
Guimarães, C. C. B., Poppiel, R. R., Barros e Souza, A., Quesada, C. A., and do Couto, H. T. Z.: The Brazilian Soil Spectral Library (BSSL): A general
view, application and challenges, Geoderma, 354, 113793,
https://doi.org/10.1016/j.geoderma.2019.05.043, 2019.
Dickson, B. L. and Scott, K. M.: Interpretation of aerial gamma-ray surveys – adding the geochemical factors, AGSO J. Aust. Geol. Geophys., 17,
187–200, 1997.
Dobos, E.: The appliction of remote sensing and teain modeling to soil characterization, in: Innovative Soil-Plant Systems for sustainable Agricultural Practices, Organization for Economic, 328–348, ISBN 9789264099715, 2003.
Domsch, H. and Giebel, A.: Estimation of soil textural features from soil
electrical conductivity recorded using the EM38, Precis. Agric., 5,
389–409, https://doi.org/10.1023/B:PRAG.0000040807.18932.80, 2004.
Dragovic, S. and Onjia, A.: Classification of soil samples according to
geographic origin using gamma-ray spectrometry and pattern recognition
methods, Appl. Radiat. Isotopes, 65, 218–224,
https://doi.org/10.1016/j.apradiso.2006.07.005, 2007.
EMBRAPA: Documentos 132 Manual de Métodos de, Embrapa, 230, 1517–2627,
2011.
Farzamian, M., Monteiro Santos, F. A., and Khalil, M. A.: Application of EM38
and ERT methods in estimation of saturated hydraulic conductivity in
unsaturated soil, J. Appl. Geophys., 112, 175–189,
https://doi.org/10.1016/j.jappgeo.2014.11.016, 2015.
Ferreira, R. G., da Silva, D. D., Elesbon, A. A. A., Fernandes-Filho, E. I., Veloso, G. V., de Souza Fraga, M., and Ferreira, L. B.: Machine learning models for streamflow regionalization in a tropical watershed, J. Environ. Manage., 280, 111713, https://doi.org/10.1016/j.jenvman.2020.111713, 2021.
Fioriob, P. R.: Estimation of Soil Properties by Orbital and Laboratory
Reflectance Means and its Relation with Soil Classification, Open Remote
Sens. J., 2, 12–23, available at: https://www.researchgate.net/publication/252662765_Estimation_of_Soil_Properties_by_Orbital_and_Laboratory_Reflectance_Means_and_its_Relation_with_Soil_Classification (last access: 9 February 2022), 2013.
Fongaro, C. T., Demattê, J. A. M., Rizzo, R., Safanelli, J. L., De Sousa Mendes, W., Dotto, A. C., Vicente, L. E., Franceschini, M. H. D., and Ustin, S. L.: Improvement of clay and sand quantification based on a novel approach
with a focus on multispectral satellite images, Remote Sens., 10, 1555, https://doi.org/10.3390/rs10101555, 2018.
Frihy, O. E., Lotfy, M. F., and Komar, P. D.: Spatial variations in heavy
minerals and patterns of sediment sorting along the Nile Delta, Egypt,
Sediment. Geol., 97, 33–41, 1995.
Geonics, E. M.: EM38 Ground Conductivity Meter Operating Manual, Geonics
Ltd., Ontario Mississauga, ON, Canada, 32, 2002.
Greve, M. B. and Malone, B. P.: High-Resolution 3-D Mapping of Soil Texture
in Denmark, High‐resolution 3‐D mapping of soil texture in Denmark, Soil. Sci. Soc. Am. J. , 77, 860–876, 2013.
Grimley, D. A., Arruda, N. K., and Bramstedt, M. W.: Using magnetic
susceptibility to facilitate more rapid, reproducible and precise
delineation of hydric soils in the midwestern USA, Catena, 58, 183–213,
https://doi.org/10.1016/j.catena.2004.03.001, 2004.
Harris, J. R. and Grunsky, E. C.: Computers & Geosciences Predictive
lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data, Comput. Geosci., 80,
9–25, https://doi.org/10.1016/j.cageo.2015.03.013, 2015.
Heil, K. and Schmidhalter, U.: Theory and Guidelines for the Application of
the Geophysical Sensor EM38, Sensors, 19, 4293, 2019.
Hendrickx, J. M., Wraith, J. M., Corwin, D. L., and Kachanoski, R. G.: Miscible solute transport, in: Methods of soil analysis. Part 4. Physical methods, edited by: Dane, J. H. and Topp, G. C., SSSA Book Series 5, Madison, WI., 1253–1321, ISBN 9780891188414, 2002.
Henrique, S., Silva, G., Silva, E. A., Poggere, G. C., Linares, A., Junior,
P., Gabriele, M., Gonçalves, M., Roberto, L., Guilherme, G., and Curi,
N.: Soils and Plant Nutrition Modeling and prediction of sulfuric acid
digestion analyses data from PXRF spectrometry in tropical soils, Sci.
Agric., 77, https://doi.org/10.1590/1678-992X-2018-0132, 2018.
Heuvelink, G. B. M. and Webster, R.: Modelling soil variation: past,
present, and future, Geoderma, 100, 269–301, 2001.
Honeyborne, I., McHugh, T. D., Kuittinen, I., Cichonska, A., Evangelopoulos,
D., Ronacher, K., van Helden, P. D., Gillespie, S. H., Fernandez-Reyes, D.,
Walzl, G., Rousu, J., Butcher, P. D., and Waddell, S. J.: Profiling
persistent tubercule bacilli from patient sputa during therapy predicts
early drug efficacy, BMC Med., 14, 1–13, https://doi.org/10.1186/s12916-016-0609-3,
2016.
Hothorn, T.: CRAN task view: Machine learning & statistical learning, https://CRAN.R-project.org/view=MachineLearning (last access: 8 February 2022), 2021.
Hounkpatin, O. K. L., Op, F., Hipt, D., Yaovi, A., Welp, G., and Amelung, W.:
Catena Soil organic carbon stocks and their determining factors in the Dano
catchment (Southwest Burkina Faso), Catena, 166, 298–309,
https://doi.org/10.1016/j.catena.2018.04.013, 2018.
IUSS Working Group WRB: World reference base for soil resources 2014 –
International soil classification system for naming soils and creating
legends for soil maps, World Soil Resources Report, 106, 12–21, 2014.
Jafarzadeh, A. A., Pal, M., Servati, M., Fazeli Fard, M. H., and Ghorbani, M.
A.: Comparative analysis of support vector machine and artificial neural
network models for soil cation exchange capacity prediction, Int. J.
Environ. Sci. Te., 13, 87–96, https://doi.org/10.1007/s13762-015-0856-4, 2016.
Javadi, S. H., Munnaf, M. A., and Mouazen, A. M.: Fusion of Vis-NIR and XRF
spectra for estimation of key soil attributes, Geoderma, 385, 114851, https://doi.org/10.1016/j.geoderma.2020.114851, 2021.
Jenny, H.: Factors of soil formation: A system of quantitative pedology,
Dover Publication, New York, USA, 1994.
Jiménez, C., Benavides, J., Ospina-Salazar, D. I., Zúñiga, O.,
Ochoa, O., and Mosquera, C.: Relationship between physical properties and the
magnetic susceptibility in two soils of Valle del Cauca Relación entre
propiedades físicas y la susceptibilidad magnética en dos suelos
del Valle del Cauca, Cauca. Rev. Cienc. Agri., 34, 33–45, https://doi.org/10.22267/rcia.173402.70, 2017.
Johnston, M. A., Savage, M. J., Moolman, J. H., and du Plessis, H. M.:
Evaluation of Calibration Methods for Interpreting Soil Salinity from
Electromagnetic Induction Measurements, Soil Sci. Soc. Am. J., 61,
1627–1633, https://doi.org/10.2136/sssaj1997.03615995006100060013x, 1997.
Jung, Y., Lee, J., Lee, M., Kang, N., and Lee, I.: Probabilistic analytical
target cascading using kernel density estimation for accurate uncertainty
propagation, Struct. Multidiscip. O., 61, 2077–2095, 2020.
Kämpf, N. and Curi, N.: Óxidos de ferro: indicadores de ambientes
pedogênicos e geoquímicos, Tóp. ciênc. solo, 1, 107–138, 2000.
Karpachevskii, L O.: A book on the pedosphere of the earth
Eurasian Soil Sci., 44, 832–833, https://doi.org/10.1134/S1064229311070088, 2011.
Kuhn, M. and Johnson, K.: Applied predictive modeling, 26, Springer, New York, 13, ISBN 9781461468493, available at: https://link.springer.com/book/10.1007/978-1-4614-6849-3 (last access: 1 February 2022), 2013.
Lacoste, M., Lemercier, B., and Walter, C.: Regional mapping of soil parent
material by machine learning based on point data, Geomorphology, 133,
90–99, https://doi.org/10.1016/j.geomorph.2011.06.026, 2011.
Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., Martin, M., and
Saby, N. P. A.: How far can the uncertainty on a Digital Soil Map be known?:
A numerical experiment using pseudo values of clay content obtained from
Vis-SWIR hyperspectral imagery, Geoderma, 337, 1320–1328, 2019.
Leng, X., Qian, X., Yang, M., Wang, C., Li, H., and Wang, J.: Leaf magnetic
properties as a method for predicting heavy metal concentrations in PM 2.5
using support vector machine: A case study in Nanjing, China, Environ.
Pollut., 242, 922–930, https://doi.org/10.1016/j.envpol.2018.07.007, 2018.
Lesch, S. M., Rhoades, J. D., Lund, L. J., and Corwin, D. L.: Mapping soil
salinity using calibrated electromagnetic measurements, Soil Sci. Soc. Am.
J., 56, 540–548, 1992.
Levi, M. R. and Rasmussen, C.: Covariate selection with iterative principal
component analysis for predicting physical soil properties, Geoderma, 219,
46–57, 2014.
Li, H., Wang, J., Wang, Q., Tian, C., Qian, X., and Leng, X.: Magnetic
Properties as a Proxy for Predicting Fine-Particle-Bound Heavy Metals in a
Support Vector Machine Approach, Environ. Sci. Technol., 51, 6927–6935,
https://doi.org/10.1021/acs.est.7b00729, 2017.
Liao, K., Xu, S., Wu, J., Zhu, Q., and An, L.: Using support vector machines
to predict cation exchange capacity of different soil horizons in Qingdao
City, China, J. Plant Nutr. Soil Sci., 177, 775–782, 2014.
Ließ, M., Glaser, B., and Huwe, B.: Uncertainty in the spatial prediction
of soil texture: Comparison of regression tree and Random Forest models,
Geoderma, 170, 70–79, https://doi.org/10.1016/j.geoderma.2011.10.010,
2012.
Lim, C. H. and Jackson, M. L.: Dissolution for total elemental analysis – Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, 9, 1–12, https://doi.org/10.2134/agronmonogr9.2.2ed.c1, 1983.
Loiseau, T., Richer-de-forges, A. C., Martelet, G., Bialkowski, A., Nehlig,
P., and Arrouays, D.: Could airborne gamma-spectrometric
data replace lithological maps as co-variates for digital soil mapping of
topsoil particle-size distribution? A case study in Western France,
Geoderma Reg., 22, e00295, https://doi.org/10.1016/j.geodrs.2020.e00295, 2020.
Malone, B. P., McBratney, A. B., Minasny, B., and Laslett, G. M.: Mapping
continuous depth functions of soil carbon storage and available water
capacity, Geoderma, 154, 138–152, https://doi.org/10.1016/j.geoderma.2009.10.007,
2009.
McFadden, M. and Scott, W. R.: Broadband soil susceptibility measurements
for EMI applications, J. Appl. Geophys., 90, 119–125,
https://doi.org/10.1016/j.jappgeo.2013.01.009, 2013.
McNeill, J. D.: Geonics EM38 ground conductivity meter,
Geonics Ltd., Mississauga, Ontario, Canada, Tech. Note TN-21, 1986.
McNeill, J. D.: Rapid, accurate mapping of soil salinity by electromagnetic
ground conductivity meters, in: Advances in measurement of soil physical properties: Bringing theory into practice, 30, edited by: Clarke Topp, G., Daniel Reynolds, W. and Green, R. E., 209–229, https://doi.org/10.2136/sssaspecpub30.c11, 1992.
Mello, D., Demattê, J. A. M., Silvero, N. E. Q., Di Raimo, L. A. D. L.,
Poppiel, R. R., Mello, F. A. O., Souza, A. B., Safanelli, J. L., Resende, M.
E. B., and Rizzo, R.: Soil magnetic susceptibility and its relationship with
naturally occurring processes and soil attributes in pedosphere, in a
tropical environment, Geoderma, 372, 114364,
https://doi.org/10.1016/j.geoderma.2020.114364, 2020.
Mello, D., Demattê, J. A. M., Alcantara de Oliveira Mello, F.,
Poppiel, R. R., Quiñonez Silvero, N. E., Safanelli, J. L.,
Barros e Souza, A., Di Loreto Di Raimo, L. A., Rizzo, R., Bispo
Resende, M. E., and Reynaud Schaefer, C. E. G. R.: Applied
gamma-ray spectrometry for evaluating tropical soil processes and
attributes, Geoderma, 381, 114736, https://doi.org/10.1016/j.geoderma.2020.114736, 2021.
Minty, B. R. S.: A Review of Airborne Gamma-Ray Spectrometric
Data-Processing Techniques, Australian Government Publishing Service, https://doi.org/10.1071/EG14110, 1988.
Montanarella, L., Pennock, D. J., McKenzie, N., Badraoui, M., Chude, V., Baptista, I., Mamo, T., Yemefack, M., Singh Aulakh, M., Yagi, K., Young Hong, S., Vijarnsorn, P., Zhang, G.-L., Arrouays, D., Black, H., Krasilnikov, P., Sobocká, J., Alegre, J., Henriquez, C. R., de Lourdes Mendonça-Santos, M., Taboada, M., Espinosa-Victoria, D., AlShankiti, A., AlaviPanah, S. K., Elsheikh, E. A. E. M., Hempel, J., Camps Arbestain, M., Nachtergaele, F., and Vargas, R.: World's soils are under threat, SOIL, 2, 79–82, https://doi.org/10.5194/soil-2-79-2016, 2016.
Mullins, C. E.: Magnetic susceptibility of the soil and its significance in
soil science–a review, J. Soil Sci., 28, 223–246, 1977.
Nanni, M. R. and Demattê, J. A. M.: Spectral Reflectance Methodology in
Comparison to Traditional Soil Analysis, Soil Sci. Soc. Am. J., 70,
393–407, https://doi.org/10.2136/sssaj2003.0285, 2006.
Narjary, B., Meena, M. D., Kumar, S., Kamra, S. K., Sharma, D. K., and
Triantafilis, J.: Digital mapping of soil salinity at various depths using
an EM38, Soil Use Manag., 35, 232–244, https://doi.org/10.1111/sum.12468, 2019.
Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., and Mouazen, A. M.:
Estimating the soil clay content and organic matter by means of different
calibration methods of vis-NIR diffuse reflectance spectroscopy, Soil
Till. Res., 155, 510–522, https://doi.org/10.1016/j.still.2015.07.021, 2016.
Neogi, S. and Dauwels, J.: Factored Latent-Dynamic Conditional Random Fields for single and multi-label sequence modeling, Pattern Recogn., 122, 108236, https://doi.org/10.1016/j.patcog.2021.108236, 2022.
O'Rourke, S. M., Stockmann, U., Holden, N. M., McBratney, A. B., and Minasny,
B.: An assessment of model averaging to improve predictive power of portable
vis-NIR and XRF for the determination of agronomic soil properties,
Geoderma, 279, 31–44, https://doi.org/10.1016/j.geoderma.2016.05.005, 2016.
Pansu, M. and Gautheyrou, J.: Handbook of Soil Analysis – Mineralogical,
Organic and Inorganic Methods, 1st edn., Springer, Netherlands, https://doi.org/10.1007/978-3-540-31211-6, 2006.
Perlich, C.: Learning Curves in Machine Learning, RC24756 (W0903-020), March 5, 2009, Computer Science, available at: https://dominoweb.draco.res.ibm.com/reports/rc24756.pdf (last access: 3 February 2022), 2010.
Pozza, L. E. and Field, D. J.: The science of soil Security and food
security, Soil Secur., 1, 100002, https://doi.org/10.1016/j.soisec.2020.100002, 2020.
Priori, S., Fantappiè, M., Bianconi, N., Ferrigno, G., Pellegrini, S.,
and Costantini, E. A. C.: Field-Scale Mapping of Soil Carbon Stock with
Limited Sampling by Coupling Gamma-Ray and Vis-NIR Spectroscopy, Soil Sci.
Soc. Am. J., 80, 954–964, https://doi.org/10.2136/sssaj2016.01.0018, 2016.
R Core Team: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://r.meteo.uni.wroc.pl/web/packages/dplR/vignettes/intro-dplR.pdf (last access: 1 February 2022), 2015.
Radiation Solutions: Spectrum stabilization and calibration for the RSI RS-125 and
RS-230 handheld spectrometers, Appendix D, 57, available at: https://www.aseg.org.au/sites/default/files/RS-125%20RS-230_User_Manual%20%28GR%29.pdf
(last access: 3 February 2022), 2009.
Reinhardt, N. and Herrmann, L.: Gamma-ray spectrometry as versatile tool in
soil science: A critical review, J. Plant Nutr. Soil Sci., 182, 9–27,
https://doi.org/10.1002/jpln.201700447, 2019.
Rhoades, J. D., Chanduvi, F., and Lesch, S. M.: Soil salinity assessment:
Methods and interpretation of electrical conductivity measurements, Food and Agriculture Organization of the United Nations, ISBN 9251042810, 1999.
Richards, L. A.: Diagnosis and improvement of saline and alkali soils, 78, 154, LWW, 1954.
Rochette, P., Jackson, M., and Aubourg, C.: Rock magnetism and the
interpretation of magnetic susceptibility, Rev. Geophys., 30, 209–226,
1992.
Rytky, S. J. O., Tiulpin, A., Frondelius, T., Finnilä, M. A. J.,
Karhula, S. S., Leino, J., Pritzker, K. P. H., Valkealahti, M., Lehenkari,
P., Joukainen, A., Kröger, H., Nieminen, H. J., and Saarakkala, S.:
Automating three-dimensional osteoarthritis histopathological grading of
human osteochondral tissue using machine learning on contrast-enhanced
micro-computed tomography, Osteoarthr. Cartilage, 28, 1133–1144,
https://doi.org/10.1016/j.joca.2020.05.002, 2020.
Sales, Support and Costomisation: Terraplus KT-10 v2 User Manual – User's Guide ver. 2.1, available at: https://www.aseg.org.au/sites/default/files/KT-10%20User%20Manual%20%28GR%29.pdf
(last access: 3 February 2022) (last access: 2 February 2022), 2021.
Sarmast, M., Farpoor, M. H., and Esfandiarpour Boroujeni, I.: Magnetic
susceptibility of soils along a lithotoposequence in southeast Iran, Catena,
156, 252–262, https://doi.org/10.1016/j.catena.2017.04.019, 2017.
Schaetzl, J. R. and Anderson, S.: Soil Genesis and Geomorphology, 1st edn.
Cambridge University Press, New York, USA, ISBN 9780521812016, 2005.
Schuler, U., Erbe, P., Zarei, M., Rangubpit, W., Surinkum, A., Stahr, K., and
Herrmann, L.: A gamma-ray spectrometry approach to field separation of
illuviation-type WRB reference soil groups in northern Thailand, J. Plant
Nutr. Soil Sci., 174, 536–544, https://doi.org/10.1002/jpln.200800323, 2011.
Schwertmann, U. and Taylor, R. M.: Iron oxides, in: Minerals in Soil Environments, 1st edn., 379–438, ISBN 9780891187875, 1989.
Shenggao, L.: Lithological factors affecting magnetic susceptibility of
subtropical soils, Zhejiang Province, China, Catena, 40, 359–373,
https://doi.org/10.1016/S0341-8162(00)00092-8, 2000.
Silva, E. B., Giasson, É., Dotto, A. C., Caten, A. T., Demattê, J. A. M., Bacic, I. L. Z., and Veiga, M. D.: A Regional Legacy Soil Dataset for Prediction of Sand and Clay
Content with Vis-Nir-Swir in Southern Brazil, Rev. Bras. Cienc. Solo, 43, 1–20, 2019.
Silvero, N. E. Q., Di Raimo, L. A. D. L., Pereira, G. S., de Magalhães, L. P., da Terra, F. S., Dassan, M. A. A., Salazar, D. F. U., and Demattê,
J. A. M.: Effects of water, organic matter, and iron forms in mid-IR spectra
of soils: Assessments from laboratory to satellite-simulated data, Geoderma,
375, 114480, https://doi.org/10.1016/j.geoderma.2020.114480, 2020.
Siqueira, D. S., Marques, J., Matias, S. S. R., Barrón, V., Torrent, J.,
Baffa, O., and Oliveira, L. C.: Correlation of properties of Brazilian
Haplustalfs with magnetic susceptibility measurements, Soil Use Manage.,
26, 425–431, https://doi.org/10.1111/j.1475-2743.2010.00294.x, 2010.
Taylor, M. J., Smettem, K., Pracilio, G., and Verboom, W.: Relationships
between soil properties and high-resolution radiometrics, central eastern
Wheatbelt, Western Australia, Explor. Geophys., 33, 95–102, https://doi.org/10.1071/EG02095, 2018.
Teixeira, P. C., Donagemma, G. K., Fontana, A., and Teixeira, W. G.: Manual
de métodos de análise de solo, Embrapa, Rio de Janeiro, Brazil, 573 pp., ISBN 9788570357717, 2017.
Terra, F. S., Demattê, J. A. M., and Viscarra Rossel, R. A.: Proximal
spectral sensing in pedological assessments: vis–NIR spectra for soil
classification based on weathering and pedogenesis, Geoderma, 318,
123–136, https://doi.org/10.1016/j.geoderma.2017.10.053, 2018.
Triantafilis, J., Lesch, S. M., La Lau, K., and Buchanan, S. M.: Field level
digital soil mapping of cation exchange capacity using electromagnetic
induction and a hierarchical spatial regression model, Aust. J. Soil Res.,
47, 651–663, https://doi.org/10.1071/SR08240, 2009.
Valaee, M., Ayoubi, S., Khormali, F., Lu, S. G., and Karimzadeh, H. R.: Using
magnetic susceptibility to discriminate between soil moisture regimes in
selected loess and loess-like soils in northern Iran, J. Appl. Geophys.,
127, 23–30, https://doi.org/10.1016/j.jappgeo.2016.02.006, 2016.
Vašát, R., Kode, R., Klement, A., and Brodský, L.: Combining reflectance spectroscopy and the digital elevation model for soil oxidizable
carbon estimation, Geoderma, 303, 133–142, https://doi.org/10.1016/j.geoderma.2017.05.018,
2017.
Viana, J. H. M., Couceiro, P. R. C., Pereira, M. C., Fabris, J. D.,
Fernandes Filho, E. I., Schaefer, C., Rechenberg, H. R., Abrahão, W. A.
P., and Mantovani, E. C.: Occurrence of magnetite in the sand fraction of an
Oxisol in the Brazilian savanna ecosystem, developed from a magnetite-free
lithology, Soil Res., 44, 71–83, 2006.
Veloso, G. V., de Mello, D. C., Guedes Lana, M., Alcantara de Oliveira Mello, F., Poppiel, R. R., Ribeiro Oquendo Cabrero, D., Di Raimo, L. A., Gonçalves Reynaud Schaefer, C. E., Fernandes-Filho, E. I., Pereira Leite, E., and Melo Demattê, J. A.: Data and script for “A new methodological framework for geophysical sensors combinations associated with machine learning algorithms to understand soil attributes” (v.1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5733366, 2021.
Viscarra Rossel, R. A., Webster, R., and Kidd, D.: Mapping gamma radiation
and its uncertainty from weathering products in a Tasmanian landscape with a
proximal sensor and random forest kriging, Earth Surf. Proc. Landforms,
39, 735–748, https://doi.org/10.1002/esp.3476, 2014.
Wilford, J. and Minty, B.: Chapter 16 The Use of Airborne Gamma-ray Imagery
for Mapping Soils and Understanding Landscape Processes, Dev. Soil Sci.,
31, 207–610, https://doi.org/10.1016/S0166-2481(06)31016-1, 2006.
Wilford, J. and Thomas, M.: Modelling soil-regolith thickness in complex
weathered landscapes of the central Mt Lofty Ranges, South Australia, ISBN 9780415621557, 2012.
Wilford, J. R., Bierwirth, P. E., and Craig, M. A.: Application of airborne gamma-ray spectrometry in soil/regolith mapping and applied geomorphology, AGSO J. Aust. Geol. Geophys., 17, 201–216, 1997.
Wong, M. T. F. and Harper, R. J.: Use of on-ground gamma-ray spectrometry to
measure plant-available potassium and other topsoil attributes, Aust. J.
Soil Res., 37, 267–277, https://doi.org/10.1071/S98038, 1999.
Xu, D., Zhao, R., Li, S., Chen, S., Jiang, Q., Zhou, L., and Shi, Z.:
Multi-sensor fusion for the determination of several soil properties in the
Yangtze River Delta, China, Eur. J. Soil Sci., 70, 162–173, 2019.
Zare, E., Li, N., Khongnawang, T., Farzamian, M., and Triantafilis, J.: Identifying potential leakage zones in an irrigation supply channel by mapping soil properties using electromagnetic induction, inversion modelling and a support vector machine, Soil Systems, 4, 25, https://doi.org/10.3390/soilsystems4020025, 2020.
Zhang, Y. and Hartemink, A. E.: Data fusion of vis – NIR and PXRF spectra
to predict soil physical and chemical properties, Eur. J. Soil Sci., 71, 316–333, https://doi.org/10.1111/ejss.12875, 2020.
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
We used soil parent material, terrain attributes, and geophysical data from the soil surface to...