El-Sandakli et al. (2026) Assessment and Monitoring of Agricultural Drought Using Remote Sensing and GeoAI

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Identification

• Journal: Advances in computational intelligence and robotics book series
• Year: 2026
• Date: 2026-09-24
• Authors: Mariah El-Sandakli, Walid Al-Shaar, Ghaleb Faour, Mohamad Al-Shaar
• DOI: 10.4018/979-8-2600-1704-3.ch004

Research Groups

This chapter reviews and synthesizes approaches for agricultural drought assessment, monitoring, and early warning, emphasizing the integration of remote sensing, Geographic Information Systems (GIS), and Geospatial Artificial Intelligence (GeoAI).

Objective

• To review and synthesize current and emerging approaches for agricultural drought assessment, monitoring, and early warning, focusing on the integration of remote sensing, GIS, and GeoAI.

Study Configuration

• Spatial Scale: Global (review of methods applicable across various spatial scales)
• Temporal Scale: Continuous (review of methods applicable for monitoring and prediction over various temporal resolutions)

Methodology and Data

• Models used: Drought indices, machine learning, deep learning
• Data sources: Traditional climate-based observations, Earth observation (vegetation, temperature, soil moisture indicators), satellite data, climate information, crop characteristics

Main Results

• The chapter synthesizes traditional, Earth observation, and GeoAI methods for agricultural drought, demonstrating that integrated approaches offer enhanced capabilities for accurate monitoring, prediction, spatial analysis, and vulnerability assessment.
• The integration of satellite data, climate information, crop characteristics, and artificial intelligence provides new opportunities for precision agriculture, improved water management, and climate resilience.

Contributions

• Provides a comprehensive synthesis of diverse methodologies for agricultural drought assessment, highlighting the original value of integrating remote sensing, GIS, and GeoAI for advancing precision agriculture, water management, and climate resilience.

Funding

Citation

@article{ElSandakli2026Assessment,
  author = {El-Sandakli, Mariah and Al-Shaar, Walid and Faour, Ghaleb and Al-Shaar, Mohamad},
  title = {Assessment and Monitoring of Agricultural Drought Using Remote Sensing and GeoAI},
  journal = {Advances in computational intelligence and robotics book series},
  year = {2026},
  doi = {10.4018/979-8-2600-1704-3.ch004},
  url = {https://doi.org/10.4018/979-8-2600-1704-3.ch004}
}