High-Resolution Wheat and Barley Yield Forecasting Using Multi-Temporal Satellite Time Series and Machine Learning

High-resolution yield forecasting is essential for advancing precision agriculture and improving the sustainability of wheat and barley production. While most previous studies focus on field-scale predictions, pixel-level approaches are needed to capture intra-field variability and support site-specific management. This paper evaluates the performance of machine learning models for 10 m resolution yield prediction using multi-temporal Sentinel-2 surface reflectance data across seven major cereal-producing regions in Spain. Yield monitor data from winter wheat and barley fields collected over five growing seasons (2020–2024) were combined with spectral bands and vegetation indices. Random Forest (RF) and XGBoost (XGB) models were trained at five phenological stages expressed as days before harvest (DBH) and validated using both internal (2020–2023) and independent external (2024) datasets. Model accuracy increased as harvest approached. In external validation, RF achieved the best performance for wheat (R2 = 0.77; RMSE ≈ 697 kg · ha−1), while XGB performed best for barley (R2 = 0.86; RMSE ≈ 744 kg · ha−1). Visible, red-edge, and SWIR bands were the most informative predictors, especially during grain filling and senescence. Results demonstrate the potential of multi-temporal Sentinel-2 data and machine learning for accurate, transferable, pixel-level yield forecasting in Mediterranean cereal systems.