Luo et al. (2026) Multi-variable remote sensing data assimilation for winter wheat water footprint: a case study in Shijiazhuang, North China

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Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-06-22
• Authors: Li Luo, Ting Bai, Shikun Sun, Ruoqing Hu, Yali Yin, Z L Zhang
• DOI: 10.1088/1748-9326/ae803d

Research Groups

• Institute of Soil Science, Chinese Academy of Sciences (ISSCAS)
• Key Laboratory of Arable Land Quality Monitoring and Management, Ministry of Agriculture and Rural Affairs (MARA)

Short Summary

This study quantifies regional winter wheat unit crop water footprint in Shijiazhuang, North China, using a parameter optimization-based data assimilation framework integrating remote sensing with a process-based crop growth model. The results reveal spatial patterns and heterogeneity of the regional winter wheat water footprint.

Objective

• Investigate the feasibility of accurately quantifying regional winter wheat unit crop water footprint in Shijiazhuang, North China, using a parameter optimization-based data assimilation framework.

Study Configuration

• Spatial Scale: Regional scale (Shijiazhuang, Huang–Huai–Hai Plain)
• Temporal Scale: Winter wheat growth period

Methodology and Data

• Models used: CERES-wheat model with multi-variable parameter optimization using the shuffled complex evolution-University of Arizona algorithm
• Data sources: Multi-source optical and radar remote sensing, time series of leaf area index and surface soil moisture

Main Results

• Regional winter wheat unit crop water footprint (CWF) ranged from 0.52 to 1.21 m^3 kg^-1.
• Higher CWF values observed in western and central parts, lower values in northeastern and southeastern regions.
• Blue and green water footprints exhibited contrasting spatial distributions.

Contributions

• This study provides a robust methodological basis for advancing the understanding of agricultural water use during crop growth.
• The results reveal the spatial patterns and heterogeneity of the regional winter wheat water footprint, addressing limitations inherent in existing approaches.

Funding

• National Natural Science Foundation of China (NSFC) project code: 41907153
• Ministry of Agriculture and Rural Affairs (MARA) project code: 2020ZX01101

Citation

@article{Luo2026Multivariable,
  author = {Luo, Li and Bai, Ting and Sun, Shikun and Hu, Ruoqing and Yin, Yali and Xue, Jing and Bian, Jiang and Zhang, Z L},
  title = {Multi-variable remote sensing data assimilation for winter wheat water footprint: a case study in Shijiazhuang, North China},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/ae803d},
  url = {https://doi.org/10.1088/1748-9326/ae803d}
}