Ahmed et al. (2025) Agricultural drought monitoring in Africa based on Self-Organizing Agricultural Drought Index

Identification

• Journal: Advances in Space Research
• Year: 2025
• Date: 2025-12-05
• Authors: N. K. Ahmed, Shuanggen Jin
• DOI: 10.1016/j.asr.2025.12.004

Research Groups

• Shanghai Astronomical Observatory, Chinese Academy of Sciences, China
• School of Astronomy and Space Science, University of Chinese Academy of Sciences, China
• Civil Engineering Department, Faculty of Engineering, Sohag University, Egypt
• School of Artificial Intelligence, Anhui University, China
• School of Surveying and Land Information Engineering, Henan Polytechnic University, China

Short Summary

This paper proposes a novel Self-Organizing Agricultural Drought Index (SOADI) using a Self-Organizing Map (SOM) technique to improve agricultural drought monitoring in Africa, demonstrating its robustness and accuracy across diverse agro-climatic zones.

Objective

• To propose and evaluate a novel Self-Organizing Agricultural Drought Index (SOADI) for agricultural drought monitoring in Africa, leveraging machine learning with the Self-Organizing Map (SOM) technique.

Study Configuration

• Spatial Scale: Two contrasting agro-climatic zones in Africa: El Faiyum, Egypt (hyper-arid), and Jowhar, Somalia (semi-arid).
• Temporal Scale: 2000 to 2023.

Methodology and Data

• Models used: Self-Organizing Map (SOM) machine learning technique to develop the Self-Organizing Agricultural Drought Index (SOADI).
• Data sources: Integrated multiple remote sensing indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII), Soil Adjusted Vegetation Index (SAVI), and Land Surface Temperature (LST).

Main Results

• A high frequency of drought events was observed in the study areas between 2000 and 2023.
• SOADI demonstrated strong correlations with the Vegetation Health Index (VHI) (Egypt: r = 0.83–0.93; Somalia: r = 0.81–0.93) and the Standardized Precipitation Index (SPI) (Egypt: r = 0.75–0.89; Somalia: r = 0.71–0.81), indicating its robustness across different climates.
• SOADI successfully detected notable historical drought events, including severe droughts in Somalia in 2011 and 2022.

Contributions

• Introduction of a novel Self-Organizing Agricultural Drought Index (SOADI) that integrates multiple remote sensing indices using a machine learning approach (SOM) for comprehensive agricultural drought assessment.
• Provides a promising tool for accurate drought monitoring, offering deeper insight into spatiotemporal drought variability within agricultural regions.
• Significantly improves upon traditional drought indices, leading to more effective drought management strategies, particularly in challenging regions like Africa.

Funding

Not specified in the provided paper excerpt.

Citation

@article{Ahmed2025Agricultural,
  author = {Ahmed, N. K. and Jin, Shuanggen},
  title = {Agricultural drought monitoring in Africa based on Self-Organizing Agricultural Drought Index},
  journal = {Advances in Space Research},
  year = {2025},
  doi = {10.1016/j.asr.2025.12.004},
  url = {https://doi.org/10.1016/j.asr.2025.12.004}
}