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.
Methods for assessment of models
| 
10 Feb 2022
Methods for assessment of models |
| 10 Feb 2022
| 10 Feb 2022
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
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Cited
13 citations as recorded by crossref.
- Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region) A. Zeyliger et al. 10.3390/s22166153
- Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset D. Mello et al. 10.1016/j.soilad.2024.100024
- Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil A. Gómez et al. 10.1080/10106049.2022.2102228
- Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: New approach using geophysical sensors, topographic variables and machine learning algorithms D. de Mello et al. 10.1016/j.geoderma.2023.116615
- Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms D. César de Mello et al. 10.1016/j.catena.2022.106370
- Sand subfractions by proximal and satellite sensing: Optimizing agricultural expansion in tropical sandy soils L. Di Raimo et al. 10.1016/j.catena.2023.107604
- Radiometric and magnetic susceptibility characterization of soil profiles: Geophysical data and their relationship with Antarctic periglacial processes, pedogenesis, and lithology D. de Mello et al. 10.1016/j.catena.2023.107427
- Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning D. Mello et al. 10.1016/j.jsames.2023.104449
- Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model A. Almalawi et al. 10.1016/j.chemosphere.2022.134960
- Clustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil B. Bastos et al. 10.1016/j.jappgeo.2022.104900
- Explorative analysis of varying spatial resolutions on a soil type classification model and it's transferability in an agricultural lowland area of Lombardy, Italy O. Adeniyi & M. Maerker 10.1016/j.geodrs.2024.e00785
- Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images R. Dias et al. 10.3390/rs16214047
- Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France D. Urbina-Salazar et al. 10.3390/rs15092410
13 citations as recorded by crossref.
- Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region) A. Zeyliger et al. 10.3390/s22166153
- Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset D. Mello et al. 10.1016/j.soilad.2024.100024
- Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil A. Gómez et al. 10.1080/10106049.2022.2102228
- Chemical weathering detection in the periglacial landscapes of Maritime Antarctica: New approach using geophysical sensors, topographic variables and machine learning algorithms D. de Mello et al. 10.1016/j.geoderma.2023.116615
- Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms D. César de Mello et al. 10.1016/j.catena.2022.106370
- Sand subfractions by proximal and satellite sensing: Optimizing agricultural expansion in tropical sandy soils L. Di Raimo et al. 10.1016/j.catena.2023.107604
- Radiometric and magnetic susceptibility characterization of soil profiles: Geophysical data and their relationship with Antarctic periglacial processes, pedogenesis, and lithology D. de Mello et al. 10.1016/j.catena.2023.107427
- Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning D. Mello et al. 10.1016/j.jsames.2023.104449
- Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model A. Almalawi et al. 10.1016/j.chemosphere.2022.134960
- Clustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil B. Bastos et al. 10.1016/j.jappgeo.2022.104900
- Explorative analysis of varying spatial resolutions on a soil type classification model and it's transferability in an agricultural lowland area of Lombardy, Italy O. Adeniyi & M. Maerker 10.1016/j.geodrs.2024.e00785
- Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images R. Dias et al. 10.3390/rs16214047
- Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France D. Urbina-Salazar et al. 10.3390/rs15092410
Latest update: 04 Nov 2024
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...
