Estimation of Crop Yield Using Remote Sensing in Zamfara State

Accurate crop yield estimation is essential for food security planning and agricultural policy formulation in Nigeria. This study estimates crop yield across smallholder farming systems in Zamfara State using vegetation indices derived from Sentinel-2 satellite imagery during the 2022 growing season. The Normalised Difference Vegetation Index, the Enhanced Vegetation Index, and the Leaf Area Index are computed at peak crop growth periods and correlated with field-measured yield data from 80 sample plots distributed across Gusau, Bungudu, and Maradun LGAs. Multiple linear regression models are developed to predict yields for sorghum and millet, the dominant staple crops in the region. Results demonstrate that Sentinel-2-derived vegetation indices explain between 71 and 84 percent of the variance in measured yields. Spatial yield maps generated from the regression models reveal significant within-area variability linked to soil fertility gradients and irrigation access. The study recommends integrating satellite-based yield monitoring into the Zamfara State Agricultural Development Programme's seasonal crop assessment framework. Keywords: crop yield, remote sensing, Sentinel-2, vegetation index, Zamfara