Kaur et al. (2025) Machine learning models for crop monitoring from optical and microwave remote sensing

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-14
• Authors: Ravneet Kaur, Raman Maini, Reet Kamal Tiwari
• DOI: 10.1016/b978-0-443-31380-6.00020-9

Research Groups

• Apex Institute of Technology, Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India
• Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, India
• Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, India

Short Summary

This chapter reviews the application of machine learning models with optical and microwave remote sensing data for crop monitoring, emphasizing the critical role of soil moisture for agricultural sustainability and outlining various sensor technologies.

Objective

• To review and discuss the application of machine learning models for crop type and soil moisture monitoring using optical and microwave remote sensing data, highlighting the role of these technologies in sustainable agriculture.

Study Configuration

• Spatial Scale: Global to regional, depending on the specific sensor and application discussed (e.g., MODIS for global, SAR for regional/local).
• Temporal Scale: Continuous or frequent monitoring over agricultural growing seasons, as enabled by various remote sensing platforms.

Methodology and Data

• Models used: Machine learning models (general discussion, specific models not detailed in the provided text).
• Data sources: Satellite remote sensing data from various optical (e.g., MODIS, Landsat 8 & 9 OLI, Sentinel-2 MSI), microwave (e.g., SMAP, Sentinel-1 SAR, SCATSAT-1, RADARSAT, ALOS PALSAR), thermal, and hyperspectral sensors.

Main Results

• The chapter highlights the critical role of remote sensing, particularly optical and microwave sensors, in monitoring crop types and soil moisture.
• It details the capabilities of various satellite platforms (e.g., SMAP, Sentinel-1/2, MODIS, Landsat) for providing essential data for agricultural management.
• The importance of soil moisture monitoring for optimized irrigation, improved crop yield, prevention of soil degradation, and sustainable agriculture is underscored, linking it to United Nations Sustainable Development Goals.

Contributions

• Provides a comprehensive review and synthesis of the state-of-the-art in applying machine learning models with optical and microwave remote sensing for crop monitoring.
• Categorizes and explains the utility of various satellite sensors for assessing crop types and soil moisture, linking these applications to sustainable agricultural practices and UN Sustainable Development Goals.

Funding

• Not specified in the provided text.

Citation

@article{Kaur2025Machine,
  author = {Kaur, Ravneet and Maini, Raman and Tiwari, Reet Kamal},
  title = {Machine learning models for crop monitoring from optical and microwave remote sensing},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-31380-6.00020-9},
  url = {https://doi.org/10.1016/b978-0-443-31380-6.00020-9}
}