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.