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

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.