Articles | Volume 15, issue 3
Geosci. Model Dev., 15, 1219–1246, 2022
Geosci. Model Dev., 15, 1219–1246, 2022

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 et al.


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
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
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.