Yao et al. (2026) Improving high-resolution soil moisture mapping in China via dense ground-based observations and multi-source data fusion

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-06-23
• Authors: Zhaoyuan Yao, Yifan Qu, Jing Tian, Bo Jiang, Jiwei Deng, Yaokui Cui
• DOI: 10.1016/j.agwat.2026.110570

Research Groups

• Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
• Beijing Key Laboratory of Spatial Information Integration & Its Applications, Beijing 100871, China
• Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences, Beijing 100101, China
• Weizhi Co., Ltd., Quanzhou 362200, China
• China Railway Design Corporation, Tianjin 300300, China

Short Summary

This study presents a two-step framework for creating high-precision soil moisture data with both 1-km spatial resolution and daily continuity in China. The proposed method includes a satellite-satellite soil moisture downscaling module and a satellite-ground soil moisture calibration module.

Objective

• Investigate the feasibility of using multi-source big data from satellites and dense stations in China to improve high-resolution soil moisture mapping.
• Develop a novel two-step random forest retrieval framework for integrating dense in situ SM measurements with remote sensing data.

Study Configuration

• Spatial Scale: 1 km spatial resolution
• Temporal Scale: Daily continuity

Methodology and Data

• Models used: Random Forest (RF)
• Data sources:
  • In situ soil moisture observations from the China Meteorological Administration’s network
  • Satellite-based microwave remote sensing data from ESA CCI Combined SM dataset
  • Remote sensing time series with high spatial resolution, including albedo, leaf area index (LAI), normalized difference vegetation index (NDVI), and land surface temperature (LST)

Main Results

• The proposed method achieves a correlation coefficient (CC) of 0.772 and a root mean square error (RMSE) of 0.073 m³•m⁻³, representing improvements of 70% in CC and 33% in RMSE compared to the original ESA CCI Combined SM data.
• The satellite-ground calibration module makes a significant contribution to accuracy improvement.

Contributions

• This study provides a novel solution for high-resolution soil moisture data production and analyzes the importance of various variables in this framework.
• The proposed method offers crucial data support for research and management in fields such as agriculture, ecology, and global change.

Funding

• This research was funded by [project name] (reference code: [code]) and [project name] (reference code: [code]).

Citation

@article{Yao2026Improving,
  author = {Yao, Zhaoyuan and Qu, Yifan and Tian, Jing and Jiang, Bo and Deng, Jiwei and Cui, Yaokui},
  title = {Improving high-resolution soil moisture mapping in China via dense ground-based observations and multi-source data fusion},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110570},
  url = {https://doi.org/10.1016/j.agwat.2026.110570}
}