The yield strikes back: Enhancing the transferability of field scale wheat and barley yield models by leveraging Sentinel-1/2

• Proposed strategy to enhance EO-based yield models’ transferability. • Thermal normalization via GDD accumulation is critical. • Developed simple yield indices based on Sentinel-2 or integrating Sentinel-1. • Indices using the SWIR B11 band show the best and most stable performances across seasons and regions. • Fusion of Sentinel-1 and −2 just enhances some regional validation. Accurate and transferable crop monitoring from remote sensing remains challenging because vegetation signals are strongly affected by phenological asynchrony, climatic variability, and sensor-specific responses. Existing approaches rely on local calibrated relationships , limiting their effectiveness in data-sparse regions. This study investigates whether models calibrated on high-quality localized reference data can generalize to other regions by stabilizing sensor–biophysical relationships. The proposed methodology integrates two components (i) thermal time normalization based on growing degree days (GDD) to reduce phenology-driven variability, and (ii) physically motivated optical and optical–SAR fusion indices designed within this normalized framework to enhance the consistency of learned relationships across contrasting environments. The approach was evaluated through within-region cross-season, and cross-region experiments. Results show that GDD normalization improves performance relative to calendar-based approaches by up to 35%. In cross-season validation, fusion-based linear models achieved R 2 > 0.60 for wheat and 0.65 in barley. Cross-region validation shows reduced but meaningful transferability, with both crops reaching R 2 ≈ 0.45, and fusion reducing RMSE by ∼ 200 kg ha -1 compared to optical-only models. Machine-learning models did not improve generalization over simple parametric fits. These findings confirm that stabilizing phenological and multi-sensor relationships is critical for transferring models from data-rich to data-limited areas, providing a foundation for scalable, global agricultural monitoring.