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<p>Drought is a recurring phenomenon in Ethiopia that has resulted in agricultural production shortfalls, leading to food insecurity and poverty. Remote sensing, advances in algorithm development, and increased cloud computing have the potential to comprehensively monitor drought conditions. In this respect, the focus of this review was to provide an overview of Earth observation (EO)-based drought studies in Ethiopia between 2011 and 2022. The Google Scholar search engine was used to collect relevant articles using keywords such as drought, agricultural drought, meteorological drought, hydrological drought, remote sensing, earth observation (EO), satellite, vegetation condition index (VCI), vegetation health index (VHI), normalized difference vegetation index (NDVI), standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and earth observation for drought monitoring in Ethiopia. The results highlight that EO-based publications are increasing, and agricultural drought studies are the most prevalent. CHIRPS rainfall and MODIS vegetation products were the most reported sensors for monitoring droughts in Ethiopia. Rainfall products were the most frequently reported input data for the computation of drought indices, accounting for nearly 47.5%, followed by vegetation-related indices (37.7%). Most (67.6%) of the publications validated their drought data, with in-situ rainfall data being the most frequent data source for validation. In Ethiopia, the most widely applied drought indices remain the SPI and VCI. While significant progress has been made in EO applications for drought monitoring, future research would benefit from prioritizing: (1) advanced integration of multivariate indicators for comprehensive drought assessment, (2) development of machine learning and deep learning frameworks for predictive drought modeling, and (3) harmonization of multi-sensor remote sensing products to enhance monitoring capabilities. This review delivers actionable insights for drought monitoring stakeholders—including researchers and decision-makers—to advance the design of sustainable resilience strategies with enhanced efficacy.</p>

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Earth observation-based drought studies in Ethiopia: a review on current state and future research directions

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Zerihun Chere,
  • Aberaw Kefyalew,
  • Moges Gtachew

摘要

Drought is a recurring phenomenon in Ethiopia that has resulted in agricultural production shortfalls, leading to food insecurity and poverty. Remote sensing, advances in algorithm development, and increased cloud computing have the potential to comprehensively monitor drought conditions. In this respect, the focus of this review was to provide an overview of Earth observation (EO)-based drought studies in Ethiopia between 2011 and 2022. The Google Scholar search engine was used to collect relevant articles using keywords such as drought, agricultural drought, meteorological drought, hydrological drought, remote sensing, earth observation (EO), satellite, vegetation condition index (VCI), vegetation health index (VHI), normalized difference vegetation index (NDVI), standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and earth observation for drought monitoring in Ethiopia. The results highlight that EO-based publications are increasing, and agricultural drought studies are the most prevalent. CHIRPS rainfall and MODIS vegetation products were the most reported sensors for monitoring droughts in Ethiopia. Rainfall products were the most frequently reported input data for the computation of drought indices, accounting for nearly 47.5%, followed by vegetation-related indices (37.7%). Most (67.6%) of the publications validated their drought data, with in-situ rainfall data being the most frequent data source for validation. In Ethiopia, the most widely applied drought indices remain the SPI and VCI. While significant progress has been made in EO applications for drought monitoring, future research would benefit from prioritizing: (1) advanced integration of multivariate indicators for comprehensive drought assessment, (2) development of machine learning and deep learning frameworks for predictive drought modeling, and (3) harmonization of multi-sensor remote sensing products to enhance monitoring capabilities. This review delivers actionable insights for drought monitoring stakeholders—including researchers and decision-makers—to advance the design of sustainable resilience strategies with enhanced efficacy.