Shukla et al. (2025) Soil mapping and categorization using fusion of satellite imagery and machine learning

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Gaurav Shukla, Divya Kumari Mishra
• DOI: 10.1016/b978-0-443-34113-7.00001-8

Research Groups

• Maharishi School of Engineering and Technology, Maharishi University of Information Technology (MUIT), Lucknow, Uttar Pradesh, India
• Birbal Sahni Institute of Palaeosciences, Lucknow, Uttar Pradesh, India

Short Summary

This chapter introduces the critical role of digital soil mapping (DSM) in creating detailed soil maps for sustainable land use, particularly in emerging nations, by leveraging satellite imagery and machine learning to overcome limitations of traditional methods.

Objective

• To highlight the importance and advantages of Digital Soil Mapping (DSM) using satellite imagery and machine learning for creating detailed soil maps to support sustainable land use and resource management.

Study Configuration

• Spatial Scale: Not specified in the provided text, discussed in a general global context.
• Temporal Scale: Not specified in the provided text, discussed in a general context of growing global population and rising demand for resources.

Methodology and Data

• Models used: Machine learning (general, specific models not detailed in this excerpt).
• Data sources: Satellite imagery.

Main Results

• Digital Soil Mapping (DSM) has been validated for its accuracy and effectiveness in soil classification and mapping across diverse regions.
• DSM offers significant advantages over traditional soil survey processes by better capturing spatial variability and accelerating the mapping process through advanced computational methods.

Contributions

• This chapter synthesizes the current understanding of Digital Soil Mapping (DSM), emphasizing its critical role in sustainable land management and its advantages over traditional methods, thereby setting the context for further detailed discussions in the book.

Funding

• Funding information is not available in the provided text.

Citation

@article{Shukla2025Soil,
  author = {Shukla, Gaurav and Mishra, Divya Kumari},
  title = {Soil mapping and categorization using fusion of satellite imagery and machine learning},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00001-8},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00001-8}
}