Proximal surface pedogeophysical characterization in Maritime Antarctica: assessing pedogeomorphological, periglacial, and landform influences

Mello, Danilo César de; Baldi, Clara Glória Oliveira; Moquedace, Cássio Marques; Oliveira, Isabelle de Angeli; Veloso, Gustavo Vieira; Gomes, Lucas Carvalho; Francelino, Márcio Rocha; Schaefer, Carlos Ernesto Gonçalves Reynaud; Fernandes-Filho, Elpídio Inácio; Júnior, Edgar Batista de Medeiros; Oliveira, Fabio Soares de; Souza, José João Lelis Leal; Ferreira, Tiago Osório; Demattê, José A. M.

doi:10.5194/gmd-18-8949-2025

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.

https://doi.org/10.5194/gmd-18-8949-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 22

Model evaluation paper

|

25 Nov 2025

Model evaluation paper |

| 25 Nov 2025

Proximal surface pedogeophysical characterization in Maritime Antarctica: assessing pedogeomorphological, periglacial, and landform influences

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 Souza, Tiago Osório Ferreira, and José A. M. Demattê

Data sets

Application of machine learning to proximal gamma-ray and magnetic susceptibility surveys in the Maritime Antarctic: assessing the influence of periglacial processes and landforms Cassio Marques Moquedace https://doi.org/10.5281/zenodo.10828281

Model code and software

Application of machine learning to proximal gamma-ray and magnetic susceptibility surveys in the Maritime Antarctic: assessing the influence of periglacial processes and landforms Cassio Marques Moquedace https://doi.org/10.5281/zenodo.10828281

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.