Multi-variable remote sensing data assimilation for winter wheat water footprint: a case study in Shijiazhuang, North China

As climate warming intensifies extreme weather, efficient agricultural water resource management is crucial. The unit crop water footprint (CWF), defined as the total volume of freshwater consumed and contaminated per unit harvested crop yield, serves as a key metric for evaluating agricultural freshwater appropriation. However, its accurate quantification at regional scales remains challenging due to scale effects, spatial heterogeneity, and limitations inherent in existing approaches. This study quantifies the regional winter wheat CWF in Shijiazhuang, a representative winter wheat-dominated agricultural region of the Huang–Huai–Hai Plain in North China, using a parameter optimization-based data assimilation framework that integrates multi-source optical and radar remote sensing with a process-based crop growth model. Specifically, the CERES-wheat model is constrained through multi-variable parameter optimization using time series of leaf area index and surface soil moisture, with parameters optimized via the shuffled complex evolution-University of Arizona algorithm, to improve grid-scale estimates of winter wheat CWF. This approach overcomes the limitations of single-method frameworks, enhances spatial resolution, and captures regional heterogeneity in water footprint patterns. Results indicated that the winter wheat CWF ranged from 0.52 to 1.21 m 3 kg −1 , with higher values observed in the western and central parts of the study area and lower values in the northeastern and southeastern regions. Blue and green water footprints exhibited contrasting spatial distributions, with blue water predominating in the southern areas and green water dominating in the northern areas. This study reveals the spatial patterns and heterogeneity of the regional winter wheat water footprint and provides a robust methodological basis for advancing the understanding of agricultural water use during crop growth.