Research Topic

Advanced Temporal and Spatial Machine Learning Methods in Soil Science

About this Research Topic

In recent years, rapid population growth and the increasing demand for food have had undesirable consequences on the environment such as land degradation, desertification, and pollution of water and soil. Therefore, there is a need to better explore and recognize the factors of sustainable use of soil and water resources. In this regard, one of the most basic information on land resources is the soil properties information. To understand the processes and dynamics that control soil properties, a proper prediction of their temporal variability and spatial distribution is needed. Soil properties have spatial and temporal variations for small and large scales, affected by inherent characteristics such as factors influencing soil parent materials and non-specific and often non-linear characteristics such as soil management, fertilization, and agronomic and cultural practices. To get a holistic understanding of how different complex and dynamic factors and processes interact, machine learning offers an innovative and promising toolbox, especially for non-linear process-systems like soils. Further, machine learning can help to compute very large data sets, for example, from remote and proximal sensing, which is often limited in numerical modeling.

This Research Topic welcomes innovative original research articles or case studies dealing with the application of machine learning methods for modelling of temporal and spatial variations of soil properties. Advanced research and field applications involving remote sensing methods or proximal soil sensing are also of interest. We welcome contributions on the following topics and beyond:

• Machine learning and white box machine learning models
• Big Data and Deep learning
• Remote and proximal sensing
• Digital soil mapping, modeling, and monitoring
• Soil properties and soil health
• Prediction accuracy of machine learning of soil properties


Keywords: Machine learning, Big Data, Deep learning, Remote sensing, Proximal sensing, Digital soil mapping, Soil properties, Soil health, Prediction accuracy


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, rapid population growth and the increasing demand for food have had undesirable consequences on the environment such as land degradation, desertification, and pollution of water and soil. Therefore, there is a need to better explore and recognize the factors of sustainable use of soil and water resources. In this regard, one of the most basic information on land resources is the soil properties information. To understand the processes and dynamics that control soil properties, a proper prediction of their temporal variability and spatial distribution is needed. Soil properties have spatial and temporal variations for small and large scales, affected by inherent characteristics such as factors influencing soil parent materials and non-specific and often non-linear characteristics such as soil management, fertilization, and agronomic and cultural practices. To get a holistic understanding of how different complex and dynamic factors and processes interact, machine learning offers an innovative and promising toolbox, especially for non-linear process-systems like soils. Further, machine learning can help to compute very large data sets, for example, from remote and proximal sensing, which is often limited in numerical modeling.

This Research Topic welcomes innovative original research articles or case studies dealing with the application of machine learning methods for modelling of temporal and spatial variations of soil properties. Advanced research and field applications involving remote sensing methods or proximal soil sensing are also of interest. We welcome contributions on the following topics and beyond:

• Machine learning and white box machine learning models
• Big Data and Deep learning
• Remote and proximal sensing
• Digital soil mapping, modeling, and monitoring
• Soil properties and soil health
• Prediction accuracy of machine learning of soil properties


Keywords: Machine learning, Big Data, Deep learning, Remote sensing, Proximal sensing, Digital soil mapping, Soil properties, Soil health, Prediction accuracy


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 March 2021 Abstract
01 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 March 2021 Abstract
01 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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