Highlights What are the main findings? Agri-Fuse reduced spectral errors by 18% in the phenology-sensitive red band compared to STARFM, effectively mitigating boundary blurring in fragmented fields. The 3-day data fusion strategy achieved the optimal efficiency-accuracy balance, maintaining high yield estimation precision (RMSE = 181 kg/ha) while cutting computational costs by 66.5%. What are the implications of the main findings? Coupling high-frequency satellite observations with EnKF assimilation effectively corrected WOFOST phenological drifts, reducing the yield estimation error (NRMSE) from 11.7% to 8.2%. This study establishes a scalable, cost-effective engineering benchmark for county-level precision agriculture, resolving the conflict between monitoring frequency and operational costs.Highlights What are the main findings? Agri-Fuse reduced spectral errors by 18% in the phenology-sensitive red band compared to STARFM, effectively mitigating boundary blurring in fragmented fields. The 3-day data fusion strategy achieved the optimal efficiency-accuracy balance, maintaining high yield estimation precision (RMSE = 181 kg/ha) while cutting computational costs by 66.5%. What are the implications of the main findings? Coupling high-frequency satellite observations with EnKF assimilation effectively corrected WOFOST phenological drifts, reducing the yield estimation error (NRMSE) from 11.7% to 8.2%. This study establishes a scalable, cost-effective engineering benchmark for county-level precision agriculture, resolving the conflict between monitoring frequency and operational costs.Abstract Accurate cotton yield estimation in arid oasis regions faces challenges from landscape fragmentation and the conflict between monitoring precision and computational costs. To address this, we developed a robust integrated framework combining multi-source remote sensing, spatiotemporal fusion, and data assimilation. To resolve spatiotemporal data gaps, the existing Agricultural Fusion (Agri-Fuse) algorithm was validated and employed to generate high-resolution time-series data, which achieved superior spectral fidelity (Root Mean Square Error, RMSE = 0.041) compared to traditional methods like Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Subsequently, high-precision Leaf Area Index (LAI) time series retrieved via the eXtreme Gradient Boosting (XGBoost) algorithm (c = 0.97) were integrated into the Ensemble Kalman Filter (EnKF)-assimilated World Food Studies (WOFOST) model. This approach significantly corrected simulation biases, improving the yield estimation accuracy (R2 = 0.86, RMSE = 171 kg/ha) compared to the open-loop model. Crucially, we systematically evaluated the trade-off between assimilation frequency and efficiency. Findings identified the 3-day fusion interval as the optimal operational strategy, maintaining high accuracy (R2 = 0.83, RMSE = 181 kg/ha) while reducing computational costs by 66.5% compared to daily assimilation. This study establishes a scalable, cost-effective benchmark for precision agriculture in complex arid environments.