Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8949-2025
© Author(s) 2025. 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-18-8949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
25 Nov 2025
Model evaluation paper |
| 25 Nov 2025
| 25 Nov 2025
Proximal surface pedogeophysical characterization in Maritime Antarctica: assessing pedogeomorphological, periglacial, and landform influences
Danilo César de Mello
CORRESPONDING AUTHOR
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Clara Glória Oliveira Baldi
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Cássio Marques Moquedace
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Isabelle de Angeli Oliveira
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Gustavo Vieira Veloso
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Lucas Carvalho Gomes
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Márcio Rocha Francelino
Department of Soil Science, Federal University of Viçosa, Viçosa, 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
Edgar Batista de Medeiros Júnior
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Fabio Soares de Oliveira
Department of Geography, Federal University of Minas Gerais, Belo Horizonte, Brazil
José João Lelis Leal Souza
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Tiago Osório Ferreira
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
José A. M. Demattê
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
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Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
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Short summary
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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.
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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.
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering...
