Shitu et al. (2025) A systematic review of advances in estimating irrigation potential in Ethiopia using GIS and remote sensing with future outlook

Identification

• Journal: Discover Applied Sciences
• Year: 2025
• Date: 2025-11-21
• Authors: Kasye Shitu, Dessalegn Worku Ayalew, Kassaw Muluye, Mekete Fentaw
• DOI: 10.1007/s42452-025-07933-y

Research Groups

• Department of Natural Resource Management, College of Agriculture and Natural Resource, Mekdela Amba University, Tulu Awlia, Ethiopia
• Center for Development Research (ZEF), University of Bonn, Bonn, Germany
• Department of Soil Resources and Watershed Management, College of Agricultural Sciences, Woldia University, Woldia, Ethiopia
• Department of Horticulture, College of Agriculture and Natural Resource, Mekdela Amba University, Tulu Awlia, Ethiopia

Short Summary

This systematic review synthesizes advances in irrigation potential estimation in Ethiopia using Geographic Information Systems (GIS) and Remote Sensing (RS), identifying significant progress while highlighting critical research gaps and future directions for sustainable water and land resource management.

Objective

• To review the advances in irrigation potential estimation research in Ethiopia using Remote Sensing (RS) and Geographic Information Systems (GIS), present identified research gaps, and provide recommendations for future research and policy applications.

Study Configuration

• Spatial Scale: Ethiopia, covering an area of 1.13 million square kilometers, located at 3°–15° N latitude and 33°−48°E longitude.
• Temporal Scale: Literature published between 2012 and 2024.

Methodology and Data

• Models used: Weighting overlay suitability models, Analytic Hierarchy Process (AHP), Fuzzy overlay suitability analysis, Inverse Distance Weighting (IDW) interpolation, Kriging.
• Data sources: Satellite imagery (e.g., Landsat 8 OLI, MODIS ET dataset), observation data (e.g., long-term monthly rainfall from meteorological stations), reanalysis data (e.g., FAO Geo Network, Harmonized World Soil Data (HWSD), Digital Elevation Model (DEM), Global Gridded Population Database, Ethiopia map server data).

Main Results

• Significant advances have been made in irrigation potential estimation research in Ethiopia using GIS and RS, primarily focusing on surface irrigation and physical land factors (land use, soil physical properties, slope).
• Key research gaps include limited consideration of micro-irrigation systems (drip, sprinkler), insufficient integration of soil chemical properties, and an over-reliance on common models like AHP Weight Overlay.
• Data limitations, such as coarse spatial resolution of soil maps and scarcity of meteorological evapotranspiration data, pose challenges, often leading to the omission of crucial factors like evapotranspiration, road proximity, and population density.
• Despite substantial research outputs, their implementation into governmental policy and decision-making for irrigation project development remains insufficient.

Contributions

• Provides a consolidated, state-of-the-art review of GIS and RS-based irrigation potential estimation in Ethiopia, bridging a critical documentation gap.
• Systematically identifies and categorizes existing research gaps, offering a clear roadmap for future studies to enhance accuracy and comprehensiveness.
• Offers specific recommendations for improving methodologies, including the need to consider all irrigation methods, incorporate soil chemical properties, utilize extensive ground truth data, and explore diverse modeling approaches.
• Emphasizes the importance of translating research findings into practical policy and decision-making to support Ethiopia's development and transformation agenda in food security and water resource management.

Funding

• This work was not supported by any funding agents.

Citation

@article{Shitu2025systematic,
  author = {Shitu, Kasye and Ayalew, Dessalegn Worku and Muluye, Kassaw and Fentaw, Mekete},
  title = {A systematic review of advances in estimating irrigation potential in Ethiopia using GIS and remote sensing with future outlook},
  journal = {Discover Applied Sciences},
  year = {2025},
  doi = {10.1007/s42452-025-07933-y},
  url = {https://doi.org/10.1007/s42452-025-07933-y}
}

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