Maniraho et al. (2025) Optimizing Agricultural Drought Monitoring in East Africa: Evaluating Integrated Soil Moisture and Vegetation Health Index (SM-VHI)

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-10-28
• Authors: Albert Poponi Maniraho, Jie Bai, Lanhai Li, Habimana Fabien, Patient Mindje Kayumba, Ogbue Chukwuka Prince, Fabien Muhirwa, Lingjie Bu
• DOI: 10.3390/rs17213560

Research Groups

Not specified in the provided text.

Short Summary

This study comprehensively analyzes the integrated Soil Moisture–Vegetation Health Index (SM-VHI) for drought detection and agricultural monitoring in East Africa, confirming its effectiveness as a reliable remote-sensing tool with strong potential to inform agricultural practices and policy for food security.

Objective

• To analyze and validate the effectiveness of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa.

Study Configuration

• Spatial Scale: East Africa
• Temporal Scale: 2000 to 2020

Methodology and Data

• Models used: Integrated Soil Moisture–Vegetation Health Index (SM-VHI) algorithm. Independent drought indicators used for correlation include Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1).
• Data sources: Remote-sensing data (for SM-VHI, SSMI, VHI), historical maize yield data.

Main Results

• A sensitivity analysis identified an optimal parameter weighting (α = 0.5) for the SM-VHI algorithm.
• The optimized SM-VHI achieved an improved drought detection accuracy with a Critical Success Index (CSI) of 0.78.
• SM-VHI exhibited strong correlations with independent drought indicators (SSMI, VHI, SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress.
• Spatial and temporal trend analyses revealed patterns of drought severity and recovery across East Africa.
• A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, indicating a strong link between enhanced vegetation/soil moisture and increased crop productivity.
• The SM-VHI effectively captured drought-induced yield variability.

Contributions

• Optimization of the SM-VHI algorithm through a sensitivity analysis, identifying an optimal parameter weighting (α = 0.5) for enhanced drought detection accuracy.
• Comprehensive validation of SM-VHI's reliability for agricultural drought detection and vegetation stress monitoring across diverse climatic conditions in East Africa.
• Demonstration of a strong positive correlation between SM-VHI anomalies and maize yield variability, highlighting its utility for agricultural productivity assessment.
• Provision of a robust and reliable remote-sensing tool with significant potential to inform agricultural practices and policy decisions for enhancing food security in East Africa.

Funding

Not specified in the provided text.

Citation

@article{Maniraho2025Optimizing,
  author = {Maniraho, Albert Poponi and Bai, Jie and Li, Lanhai and Fabien, Habimana and Kayumba, Patient Mindje and Prince, Ogbue Chukwuka and Muhirwa, Fabien and Bu, Lingjie},
  title = {Optimizing Agricultural Drought Monitoring in East Africa: Evaluating Integrated Soil Moisture and Vegetation Health Index (SM-VHI)},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17213560},
  url = {https://doi.org/10.3390/rs17213560}
}