Articles | Volume 15, issue 3
Methods for assessment of models
10 Feb 2022
Methods for assessment of models |  | 10 Feb 2022

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-153', Juan Antonio Añel, 10 Aug 2021
    • AC1: 'Reply on CEC1', Danilo Mello, 16 Aug 2021
  • RC1: 'Comment on gmd-2021-153', Anonymous Referee #1, 08 Oct 2021
    • CC1: 'Reply on RC1', Danilo Mello, 15 Nov 2021
  • RC2: 'Comment on gmd-2021-153', Anonymous Referee #2, 05 Nov 2021
    • CC2: 'Reply on RC2', Danilo Mello, 15 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Danilo Mello on behalf of the Authors (13 Dec 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (16 Dec 2021) by Rohitash Chandra
AR by Danilo Mello on behalf of the Authors (21 Dec 2021)  Manuscript 
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.