09 Jun 2022
09 Jun 2022
Status: a revised version of this preprint is currently under review for the journal SOIL.

Weathering intensities in tropical soils evaluated by machine learning, clusterization and geophysical sensors

Danilo César de Mello1, Tiago Osório Ferreira2, Gustavo Vieira Veloso1, Marcos Guedes de Lana1, Fellipe Alcantara de Oliveira Mello2, Luis Augusto Di Loreto Di Raimo2, Diego Ribeiro Oquendo Cabrero3, José João Lelis Leal de Souza1, Elpídio Inácio Fernandes-Filho1, Márcio Rocha Francelino1, Carlos Ernesto Gonçalves Reynaud Schaefer1, and José A. M. Demattê2 Danilo César de Mello et al.
  • 1Department of Soil Science, Federal University of Viçosa, Viçosa, postal code 36.570-000, Brazil
  • 2Department of Soil Science, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Piracicaba, postal code 13418-900, Brazil
  • 3Geography Department of Federal University of Mato Grosso do Sul, Três Lagoas, postal code 79610-100, Brazil

Abstract. Weathering is widely used for pedogenesis and soil fertility studies, once it affects several soil attributes. Understanding the intensities of weathering can provide answers for environmental issues, soil and geosciences studies. Recently, there are available geotechnologies (such as geophysics and machine learning algorithms) that can be applied in soil science to provide pedosphere information. In this research, we performed a method to evaluate weathering intensity in a heterogeneous tropical area by proximal remote sensing data acquired by geophysical and satellite images respectively. The area is located in southwest Brazil, with 184ha and we sampled 79 sites (all with soil analysis) using toposequence knowledge. Afterwards, the principal component analysis and the ideal number of clusters was determined. Then, we determine and used the ideal number of clusters, weathering index, as input data in four modelling (prediction and spatialization) algorithms to infer different weathering intensities in soils formed from the same soil parent material. The results showed that the best model performance was for the random forest reaching 3 clusters as the ideal number. The surface pixel reflectance acquired from a Synthetic Soil Image, the terrain surface convexity and digital elevation model were the covariates that most contributed to modelling processes. The model’ specificity was greater than sensitivity. The East areas over diabase such as the Nitisol presented greater weathering intensity than the Nitisol over West diabase areas. The areas over siltite/metamorphosed siltite and Lixisols presented moderate weathering rates. The relief and topographic position strongly affected the weathering, once they controlled the hydric dynamics. The geophysical variables were related to soil attributes and weathering, which contributed to modelling and clusterization processes. The different weathering rates are mainly modulated by geomorphic processes that relief, topographic position, and the associated soil types control water dynamic at the landscape and directly affect the weathering intensities.

Danilo César de Mello et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on soil-2022-17', Sara Ramos Santos, 26 Jul 2022
    • CC2: 'Reply on CC1', Danilo Mello, 01 Aug 2022
  • CC3: 'Comment on soil-2022-17', Guilherme Oliveira, 31 Aug 2022
    • AC1: 'Reply on CC3', Danilo Mello, 20 Oct 2022
  • CC4: 'Comment on soil-2022-17', Rafael Siqueira, 05 Sep 2022
    • AC2: 'Reply on CC4', Danilo Mello, 20 Oct 2022
  • EC1: 'Comment on soil-2022-17', Nicolas P.A. Saby, 05 Sep 2022
    • AC3: 'Reply on EC1', Danilo Mello, 20 Oct 2022
  • RC1: 'Comment on soil-2022-17', Anonymous Referee #1, 15 Sep 2022
    • AC4: 'Reply on RC1', Danilo Mello, 20 Oct 2022

Danilo César de Mello et al.

Danilo César de Mello et al.


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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.