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Article

A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies

by
Gustavo Vieira Veloso
1,
Danilo César de Mello
1,
Heitor Paiva Palma
1,
Murilo Ferre Mello
1,
Lucas Vieira Silva
1,
Elpídio Inácio Fernandes-Filho
1,
Márcio Rocha Francelino
1,
Tiago Osório Ferreira
2,
José Cola Zanuncio
3,
Davi Feital Gjorup
1,
Roney Berti de Oliveira
4,
Marcos Rafael Nanni
4,
Renan Falcioni
4,* and
José A. M. Demattê
2
1
Department of Soil Science, Federal University of Viçosa, Campus Universitário, Av. Peter Henry Rolfs s/n, Viçosa 36570-900, MG, Brazil
2
Department of Soil Science, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo, Av. Pádua Dias, 11, CP 9, Piracicaba 13418-900, SP, Brazil
3
Department of Entomology, Federal University of Viçosa, Campus Universitário, Av. Peter Henry Rolfs s/n, Viçosa 36570-900, MG, Brazil
4
Graduate Program in Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, PR, Brazil
*
Author to whom correspondence should be addressed.
Soil Syst. 2025, 9(4), 124; https://doi.org/10.3390/soilsystems9040124
Submission received: 21 August 2025 / Revised: 17 October 2025 / Accepted: 4 November 2025 / Published: 8 November 2025
(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)
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Abstract

Understanding soil variability supports improved land use and soil security. This study aimed to generate uniform geophysical classes by integrating data from three proximal geophysical sensors with synthetic soil and satellite images using machine learning, proposing a soil survey protocol. Geophysical data—natural gamma-ray emissions (eU, eTh, K40), magnetic susceptibility (κ), and apparent electrical conductivity (ECa)—were collected in Piracicaba, Brazil, and clustered into homogeneous geophysical-isoparameter classes. These classes were modeled alongside Synthetic Soil Images (SYSIs), Sentinel-2 (0.45–2.29 μm), Landsat (0.43–12.51 μm) imagery, and morphometric variables. Empirical validation compared the resulting geophysical-isoparameter map with conventional pedological and lithological maps. The Support Vector Machine (SVM) algorithm exhibited the best classification performance. Results demonstrated that geophysical sensors quantitatively and qualitatively capture soil attributes linked to formation processes and types. The geophysical-isoparameter map correlated well with pedological and lithological patterns. The proposed protocol offers soil scientists a practical tool to delineate soil and lithological units using combined sensor data. Promoting collaboration among pedologists, pedometric mappers, and remote sensing experts, this approach presents a novel framework to enhance soil survey accuracy and efficiency.
Keywords: pedometrics; pedology; machine learning pedometrics; pedology; machine learning

Share and Cite

MDPI and ACS Style

Veloso, G.V.; Mello, D.C.d.; Palma, H.P.; Mello, M.F.; Silva, L.V.; Fernandes-Filho, E.I.; Francelino, M.R.; Ferreira, T.O.; Zanuncio, J.C.; Gjorup, D.F.; et al. A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies. Soil Syst. 2025, 9, 124. https://doi.org/10.3390/soilsystems9040124

AMA Style

Veloso GV, Mello DCd, Palma HP, Mello MF, Silva LV, Fernandes-Filho EI, Francelino MR, Ferreira TO, Zanuncio JC, Gjorup DF, et al. A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies. Soil Systems. 2025; 9(4):124. https://doi.org/10.3390/soilsystems9040124

Chicago/Turabian Style

Veloso, Gustavo Vieira, Danilo César de Mello, Heitor Paiva Palma, Murilo Ferre Mello, Lucas Vieira Silva, Elpídio Inácio Fernandes-Filho, Márcio Rocha Francelino, Tiago Osório Ferreira, José Cola Zanuncio, Davi Feital Gjorup, and et al. 2025. "A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies" Soil Systems 9, no. 4: 124. https://doi.org/10.3390/soilsystems9040124

APA Style

Veloso, G. V., Mello, D. C. d., Palma, H. P., Mello, M. F., Silva, L. V., Fernandes-Filho, E. I., Francelino, M. R., Ferreira, T. O., Zanuncio, J. C., Gjorup, D. F., Oliveira, R. B. d., Nanni, M. R., Falcioni, R., & Demattê, J. A. M. (2025). A Framework Based on Isoparameters for Clustering and Mapping Geophysical Data in Pedogeomorphological Studies. Soil Systems, 9(4), 124. https://doi.org/10.3390/soilsystems9040124

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