Abstract

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 approximate to 0.45, and fusion reducing RMSE by similar to 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.