Special Issue on Machine-Learning Approaches for Geoscience Modelling: Soil, Crop and Environmental Properties and Processes

Published 08 April, 2021

Over the past decade, the application of machine-learning (ML) techniques has proved successful in several fields of science. In Geosciences, the increasing availability of proximal sensors, remote sensing imagery and large monitoring networks (e.g., the Land Use and Coverage Area Frame Survey - LUCAS) has galvanised the use of ML technologies to improve geoscientific modelling, positively impacting social and business markets. Data-driven approaches, which are fundamental to ML, can help to explain poorly-understood and highly-uncertain natural phenomena and overcome some of the limitations associated with weaker justified assumptions such as linearity, stationarity and gaussianity. However, there is a need to develop new strategies to ease the application of ML in research and practice; for example, the development of reproducible workflows and easy-to-interpret, non-black box methods and new approaches to computational and uncertainty quantification problems in Geosciences.

This special issue will present expert opinions and recent applications of ML techniques for geoscientific modelling of soil, crop and environmental properties and processes. We welcome contributions that aim to compare ML models with geostatistical methods and spatio-temporal statistics in the context of soil, crop and environmental science. We will accept research articles, short communications, review papers and opinion papers.

Topics Covered:

  • Innovative approaches to the spatial prediction of environmental features and processes
  • Hyperparameter tuning via reproducible frameworks
  • Ensemble modelling
  • Novel machine-learning approaches for soil and crop monitoring
  • Explainable machine-learning workflows (non-black box approaches)

Important Deadlines:

  • Submission deadline: 30 April 2022

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select VSI: Machine Learning for Geoscience Modelling on submission. As Artificial Intelligence in Geosciences has waived its open access article publishing charge (APC) until December 2021, all articles featured in this special issue will be published free of charge. 

Guest Editors:

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