Drought Impact on Maize Yields in South Africa Mapped via Google Earth Engine and Python-Based Remote Sensing

Abstract

Climate change has highlighted global trends in droughts that directly affect rain‑fed agricultural food
availability. Maize, a core crop in South Africa, plays a significant role in regional food value chains and is
highly sensitive to rainfall variability. Despite the importance of this relationship, no spatially explicit timeseries
analysis of drought indicators and maize yield outcomes has yet been documented. This gap was
addressed by estimating the impact of drought on maize yield in South Africa through remote sensing data
analyzed within Google Earth Engine (GEE) using Python. NDVI from MODIS, rainfall from CHIRPS, and land
surface temperature served as the primary variables. Time series data spanning 2015–2018—encompassing
both El Niño and non‑El Niño years—for key maize-producing provinces such as Mpumalanga and the Free
State were extracted. These datasets were merged with FAO and Stats SA maize yield data and examined using
correlation analysis, linear regression, and anomaly detection methods. A clear negative correlation emerged
between temperature and drought severity with NDVI during El Niño seasons, accompanied by statistically
significant yield losses. Visualization via Geemap and folium revealed spatial patterns of drought intensity and
yield reductions. These findings underscore the potential of open-source tools to support policy development
and climate-resilient agricultural planning in vulnerable communities.