A High‐Precision Crop Water Footprint Quantification Framework Based on Data Assimilation

Abstract Agricultural production is a major consumer of water resources, and the crop water footprint (CWF) serves as a comprehensive metric for assessing agricultural water use efficiency and its associated impacts, thereby providing new insights for agricultural water management. However, quantitative studies of regional CWF require extensive ground observations and are often constrained by scale effects, limited accuracy, and spatiotemporal discontinuities. To address these limitations, we developed a high‐precision CWF quantification framework that assimilates remotely sensed leaf area index and downscaled soil moisture into the World Food Studies crop model using the Ensemble Kalman Filter. Application of the proposed framework in the Hetao Irrigation District successfully mapped the high‐precision maize production water footprint, revealing a spatial pattern characterized by higher values in the eastern and western regions and lower values in the central area. The mean green water footprint, blue water footprint, and total water footprint of maize were 0.045 m 3 /kg, 0.660 m 3 /kg, and 0.705 m 3 /kg, respectively. Compared with estimates derived from remote sensing evapotranspiration products and the FAO Penman–Monteith method, the data assimilation framework improved the accuracy and spatial representativeness of maize water footprint estimation. Overall, the proposed framework provides a reliable tool for quantifying agricultural water‐use efficiency and lays a data and methodological foundation for refined water resources management.