Analyzing the Correlation Between NDVI and Rainfall in Various Regions of Kenya

Abstract

Kenya is a climatically diverse country in East Africa, known for its expansive grasslands and agricultural reliance. However, frequent and intensifying droughts pose serious challenges to food security, particularly in regions dependent on rain-fed agriculture. With vegetation growth closely tied to rainfall, prolonged droughts have devastating consequences and have led to 26% of the Kenyan population being food insecure. To better estimate and mitigate the effects of drought, it is essential to understand how rainfall variability influences vegetation cover across Kenya’s distinct regions. This study investigates that relationship by analyzing rainfall and vegetation data from four ecologically and climatically diverse counties: Garissa, Kitui, Narok, and Turkana. Focusing on these select counties allowed for a more in-depth examination of regional differences. For instance, Turkana experiences a subtropical steppe climate with an average temperature of 30°C, while Narok has a cooler marine west coast climate averaging 17°C. This study spans an eight-year period (2016–2024), enabling the identification of both seasonal patterns and long-term trends while minimizing the chance of confounding variables. Satellite remote sensing data products from Google Earth Engine were used, specifically MODIS NDVI data to represent vegetation greenness and CHIRPS data for rainfall estimates. The results revealed a strong correlation between rainfall and NDVI, with an average R² value of 0.53 across the counties studied. These findings suggest that regional climate conditions modulate the strength of rainfall-vegetation interactions and highlight the value of geospatial tools in monitoring drought impacts and informing adaptive responses.