Zhai et al. (2025) SMCR: A first satellite-derived all-weather daily/1-km Soil Moisture Climatological Record (1980–2023)

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-12-13
• Authors: Shixian Zhai, Pei Leng, Chunfeng Ma, Abba Aliyu Kasim, Tian Ma, Hongyuan Huo, Si‐Bo Duan, Xiangyang Liu, Zhao-Liang Li
• DOI: 10.1038/s41597-025-06432-4

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
• Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences
• Department of Environmental Resources Management, Faculty of Earth and Environmental Sciences, Federal University Dustin-Ma
• College of Architecture and Civil Engineering, Beijing University of Technology

Short Summary

This paper develops the first satellite-derived global all-weather daily/1-kilometer Soil Moisture Climatological Record (SMCR) spanning 1980–2023, achieving an average unbiased root mean square error of 0.051 m³/m³ against in-situ observations.

Objective

• To develop the first satellite-derived global daily fine-resolution (1 km) Soil Moisture Climatological Record (1980–2023) with all-weather seamless coverage, incorporating sub-grid variability of hydraulic parameters and conducting a comprehensive validation including a Bayesian credibility analysis.

Study Configuration

• Spatial Scale: Global, 1 kilometer (km) resolution.
• Temporal Scale: Daily, 1980–2023 (44 years).

Methodology and Data

• Models used: Triple Collocation Analysis (TCA) for data fusion, physically informed downscaling approach considering sub-grid variability of hydraulic properties (based on Montzka et al. 2018), Bayesian Bootstrap method for credibility analysis.
• Data sources: ESA CCI SM (v09.1), ERA5 (0–7 cm layer), GLEAM (v3.8a, surface layer), GLDAS-Noah (0–10 cm layer), SoilGrids (1 km resolution soil texture and hydraulic parameters), International Soil Moisture Network (ISMN), Heihe Integrated Observation Network (WATERNET).

Main Results

• Developed the first satellite-derived global all-weather daily/1-km Soil Moisture Climatological Record (SMCR) for the period 1980–2023.
• Achieved an averaged unbiased root mean square error (ubRMSE) of 0.051 m³/m³ when validated against 372 global in-situ observations.
• A Bayesian statics-based credibility analysis of the validated accuracy at four dense observation networks (REMEDHUS, TWENTE, OZNET, WATERNET) yielded an averaged credibility of 83.5%.
• The downscaled 1 km resolution SM data significantly improved the representation of spatial details and boundary definition compared to the original 25 km input.
• Overall accuracy of downscaled SM data consistently improved over time, with the 2019–2023 period demonstrating the highest accuracy (median R > 0.85, median ubRMSE ~0.047 m³/m³).
• Seasonal variations in accuracy were observed, with the highest in spring (median R ~0.90, ubRMSE 0.035–0.045 m³/m³) and a decrease in winter (median R ~0.75, ubRMSE 0.045–0.055 m³/m³).

Contributions

• Presents the first satellite-derived global daily/1-km Soil Moisture Climatological Record spanning over 40 years (1980–2023), addressing the lack of long-term, fine-resolution SM datasets.
• Employs a physically informed downscaling framework that explicitly incorporates high-resolution soil property data and accounts for sub-grid heterogeneity in hydraulic parameters, overcoming limitations of previous methods (e.g., reliance on optical/thermal observations or limited temporal span).
• Introduces a novel Bayesian statics-based credibility analysis for accuracy assessment at the network scale, enhancing the evaluation of the proposed SM climatological record.
• Provides a spatially continuous, all-weather dataset, filling data gaps in the original CCI SM product and offering crucial support for global climate change analysis, agricultural drought monitoring, and hydrological modeling.

Funding

• National Natural Science Foundation of China (grant 42271384)
• Central Public Interest Scientific Institution Basal Research Fund (grant 1610132022010)
• Key Project of Natural Science Foundation of Gansu Province (Grant 24JRRA082)
• State Key Laboratory of Efficient Utilization of Arable Land in China (grant EUAL-2024-02)

Citation

@article{Zhai2025SMCR,
  author = {Zhai, Shixian and Leng, Pei and Ma, Chunfeng and Kasim, Abba Aliyu and Ma, Tian and Huo, Hongyuan and Duan, Si‐Bo and Liu, Xiangyang and Li, Zhao-Liang},
  title = {SMCR: A first satellite-derived all-weather daily/1-km Soil Moisture Climatological Record (1980–2023)},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06432-4},
  url = {https://doi.org/10.1038/s41597-025-06432-4}
}