A Low-Complexity, County-Scale Yield Prediction Method for Maize and Sunflower Using Harmonized Landsat–Sentinel (HLS) Data

Accurate crop yield information is crucial for regional agricultural monitoring; however, many existing approaches rely on complex models or extensive input datasets. This study presents a low-complexity method for estimating county-level maize and sunflower yields in Fejér County, Hungary, using Harmonized Landsat–Sentinel (HLS) Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial resolution. Seasonal NDVI profiles were smoothed using a double-Gaussian fitting approach. Two modelling strategies were investigated: a robust approach using all agricultural pixels and a crop-specific approach restricted to maize or sunflower pixels. The models were tested through leave-one-year-out cross-validation against official yield statistics. For maize, the crop-specific predictive model provided the most accurate estimates (R² = 0.997; mean absolute percentage error (MAPE) = 2.0%). The MAPE remained below 4% even about 30–50 days before the end of harvest. For sunflower, the highest accuracy was obtained using the robust predictive model (R² = 0.928; MAPE = 2.73%). All models showed stable performance across years, including the extreme drought year of 2022. These findings indicate that a simple NDVI-based method can provide reliable county-scale yield estimates and may serve as a practical component in regional monitoring or early-warning systems.