Yan et al. (2026) Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-02-19
• Authors: Qiu-Yu Yan, Shixian Zhai, Pei Leng, Chunfeng Ma, Abba Aliyu Kasim, Seokhyeon Kim, Qian-Yu Liao, Tian Ma, Yun-Jing Geng, Shiyuan Fu, Muhannad Adnan Siddique, Yayong Sun, Jian-Wei Ma, Xiaoning Song, Zhao-Liang Li
• DOI: 10.1016/j.agwat.2026.110232

Research Groups

• State Key Laboratory of Efficient Utilization of Arable Land in China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, China
• Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, China
• Department of Environmental Resources Management, Faculty of Earth and Environmental Sciences, Federal University Dustin-Ma, Nigeria
• Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Republic of Korea
• College of Resources and Environment, University of Chinese Academy of Sciences, China
• Remote Sensing & Spatial Analytics (RSA) Lab, Information Technology University (ITU), Pakistan
• China Institute of Water Resources and Hydropower Research, China

Short Summary

This study proposes a framework to determine high-resolution irrigation water use (IWU) across China using multiple soil moisture (SM) observations, identifying the optimal SM depth and an effective data fusion strategy for improved estimation. The framework demonstrates that surface SM (0–10 cm) combined with an optimal fusion strategy yields the most accurate IWU estimates at a 1 km resolution.

Objective

• To investigate the optimal soil moisture (SM) depth for estimating irrigation water use (IWU) at a fine spatial resolution (1 km) across irrigated farmlands in China.
• To determine a preferred model optimization scheme (parametric vs. iterative) for IWU estimation using the SM2RAIN model.
• To develop and implement an optimal data fusion strategy for multiple satellite-derived SM products to enhance the accuracy and reliability of IWU estimates.

Study Configuration

• Spatial Scale: Kilometer scale (1 km resolution) across the entire irrigated farmlands of China.
• Temporal Scale: 10 years (2010–2019) for IWU estimation, with a calibration period from 2000–2009 for model parameters.

Methodology and Data

• Models used:
  • SM2RAIN model (for IWU estimation based on water balance principle)
  • SCE-UA (Shuffled Complex Evolution Algorithm developed at The University of Arizona) for iterative parameter optimization
  • SNR-opt (Signal-to-Noise Ratio optimization) for data fusion
• Data sources:
  • Soil Moisture (SM):
    • SMCI (Soil Moisture of China by in situ data): 1 km/daily, 10 layers (0–100 cm), 2000–2022, based on in-situ observations and ERA5-Land.
    • Four additional satellite-derived 1 km SM products: SMZHANG (XGBoost, ERA5-Land), SMZHENG (RF, CCI; ERA5), SMZHAI (sub-grid variability, CCI; ERA5; GLEAM; GLDAS), SMSONG (DISPATCH, AMSR-2/AMSR-E).
  • Evapotranspiration (ET): PML-V2 (Penman–Monteith–Leuning Version 2) model: 500 m/daily, 2000–2020, calibrated with flux tower observations, resampled to 1 km.
  • Precipitation (PRE): HRLT (High-Resolution and Long-Term) product: 1 km/daily, 1961–2019, based on CMA data and machine learning.
  • Validation Data: Annual IWU statistics from China’s provincial water resources bulletins (2010–2019).
  • Irrigated Farmland Map: Derived from Zhang et al. (2022b) for 2010–2019.

Main Results

• The top soil layer (0–10 cm) is optimal for estimating IWU, showing the strongest correlation and lowest error compared to deeper layers.
• The parametric approach for determining SM2RAIN parameters (Z*, a, b) outperformed the SCE-UA iterative optimization scheme for IWU estimation in China.
• Using SM at 0–10 cm with the parametric approach, IWU estimates achieved a correlation coefficient (R) of 0.79 and an unbiased root mean square error (ubRMSE) of 4.14 km³/year against statistical data.
• The SNR-opt data fusion strategy, incorporating all five satellite-derived SM products, significantly improved IWU estimates, reducing the ubRMSE by approximately 10% to 3.72 km³/year and increasing R to 0.83.
• The quantity of merging data had a greater influence on fusion outcomes than the precision of individual datasets, with more inputs leading to better accuracy.
• The estimated IWU values, while showing a slight underestimation (bias of -2.16 km³/year) due to the exclusion of water conveyance losses in the model, demonstrated reliable accuracy.

Contributions

• Pioneering investigation into the optimal soil moisture depth (0–10 cm) for large-scale IWU determination using multi-depth, high-resolution (1 km) SM products.
• Comparative evaluation of parametric and iterative optimization schemes for SM2RAIN parameters at a fine spatial resolution, identifying the superior performance of the parametric approach for China.
• Development and application of an advanced SNR-opt data fusion strategy to integrate multiple satellite-derived SM products, significantly reducing uncertainties and enhancing IWU estimation accuracy.
• Generation of a high-resolution (1 km) annual IWU dataset for China's irrigated farmlands from 2010 to 2019, providing valuable data for agricultural water resource management.
• Achieved markedly enhanced accuracy (RMSE of 4.51 km³/year) compared to previous studies that predominantly relied on coarser resolution data for IWU estimation.

Funding

• National Natural Science Foundation of China (grant 42271384)
• Science & Technology Fundamental Resources Investigation Program (grant 2025FY101301)
• State Key Laboratory of Efficient Utilization of Arable Land in China (grant EUAL-2025–02)

Citation

@article{Yan2026Determination,
  author = {Yan, Qiu-Yu and Zhai, Shixian and Leng, Pei and Ma, Chunfeng and Kasim, Abba Aliyu and Kim, Seokhyeon and Liao, Qian-Yu and Ma, Tian and Geng, Yun-Jing and Fu, Shiyuan and Siddique, Muhannad Adnan and Sun, Yayong and Ma, Jian-Wei and Song, Xiaoning and Li, Zhao-Liang},
  title = {Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110232},
  url = {https://doi.org/10.1016/j.agwat.2026.110232}
}

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