Jiang et al. (2025) The Global 9 km Soil Moisture Estimation by Downscaling of European Space Agency Climate Change Initiative Data from 1978 to 2020

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Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-07
• Authors: Hongtao Jiang, Hao Liu, Huanfeng Shen, Xinghua Li, Jingan Wu, Tianyi Song, Sanxiong Chen
• DOI: 10.3390/w17243471

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study downscales the European Space Agency Climate Change Initiative (CCI) soil moisture data to a 9 km spatial resolution for a 43-year period (1978-2020) using a spatiotemporal fusion model, demonstrating improved spatial detail while maintaining comparable temporal accuracy to the original data.

Objective

• To downscale the European Space Agency Climate Change Initiative (CCI) soil moisture data to a 9 km spatial resolution for a 43-year period (1978-2020) to enhance its applicability at regional scales.

Study Configuration

• Spatial Scale: Global, 9 km resolution (downscaled from approximately 25 km).
• Temporal Scale: 43 years (1978-2020).

Methodology and Data

• Models used: Spatial temporal fusion model.
• Data sources: European Space Agency Climate Change Initiative (CCI) soil moisture data, in-situ data (for evaluation).

Main Results

• The downscaled 9 km soil moisture data exhibits more detailed spatial information compared to the original CCI data.
• The downscaled data effectively captures the temporal variation (anomalies) of the CCI data.
• Evaluations against in-situ data indicate that the temporal accuracies of the downscaled data (correlation coefficient, r = 0.676; unbiased root mean square error, μbRMSE = 0.069 m³/m³) are comparable to the original CCI data (r = 0.670; μbRMSE = 0.070 m³/m³).
• Overall, the downscaled data improves the spatial resolution of CCI data and inherits its temporal accuracy with a slight improvement.

Contributions

• Generation of a 43-year (1978-2020) global 9 km resolution soil moisture dataset by downscaling CCI data.
• Improvement of the spatial resolution of CCI soil moisture data, enhancing its potential for regional applications.
• Enrichment of soil moisture downscaling techniques through the developed spatial temporal fusion model.

Funding

Not explicitly stated in the provided text.

Citation

@article{Jiang2025Global,
  author = {Jiang, Hongtao and Liu, Hao and Shen, Huanfeng and Li, Xinghua and Wu, Jingan and Song, Tianyi and Chen, Sanxiong},
  title = {The Global 9 km Soil Moisture Estimation by Downscaling of European Space Agency Climate Change Initiative Data from 1978 to 2020},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17243471},
  url = {https://doi.org/10.3390/w17243471}
}