From MODIS to Sentinel-2: A regional comparative analysis of crop-yield prediction with matched spatiotemporal data

Large-scale crop yield mapping has long relied on the Moderate Resolution Imaging Spectrometer (MODIS) due to its high temporal resolution and consistent atmospheric correction. The Sentinel-2 constellation, with its finer spatial resolution and vegetation-sensitive spectral bands, now offers new opportunities for regional- and field-scale yield prediction—especially as MODIS nears the end of its operational life. However, it remains unclear whether Sentinel-2 can ensure continuity of MODIS-based estimates across diverse agricultural regions. We present a regional sensor-to-sensor comparison of MODIS and Sentinel-2 for crop yield prediction using matched spatiotemporal inputs across two agro-ecological zones. Using reproducible regression workflows, we demonstrate that Sentinel-2 captures finer spatial variation in crop phenology and consistently outperforms MODIS in terms of predictive accuracy. For cotton, Sentinel-2 achieved an RMSE of 123.52 lb/acre and R ² of 0.76, versus MODIS with 129.20 lb/acre and R ² of 0.74. For corn, Sentinel-2 achieved 8.40 Bu/acre and 0.79, outperforming MODIS at 8.69 and 0.66, respectively. SHAP analysis identifies Enhanced Vegetation Index (EVI), Fraction of Photosynthetically Active Radiation, and Leaf Area Index as key predictors across both sensors. Despite its lower temporal frequency, Sentinel-2 delivers robust, regionally consistent estimates, supporting its suitability as a successor to MODIS for operational crop monitoring.