Chen et al. (2025) Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-10-28
• Authors: Liwen Chen, Yangguang Wang, Jingquan Ren, Haiqing Song, Guangxin Zhang, Jingxuan Sun, Lijun Wang, Jia Mu, Cong Liu, Mei-Yu Wang
• DOI: 10.1038/s41597-025-05986-7

Research Groups

• School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun, China
• Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
• Institute of Meteorological Science of Jilin Province, Changchun, China
• Ecological and Agricultural Meteorology Center of Inner Mongolia Autonomous Region, Hohhot, China
• Korqin Right Front Banner Agricultural and Animal Husbandry Science and Technology Development Center, Korqin Right Front Banner, Zhengzhou, China

Short Summary

This study developed and validated a high-accuracy, multi-layer soil moisture dataset (DMSM) for the black soil region of China, spanning 16 years (2008–2023) at a daily, 2 km resolution. The dataset, generated using an enhanced Community Land Model 3.5, demonstrated excellent performance against in-situ observations and effectively captured soil moisture dynamics, providing crucial data for agricultural management.

Objective

• To develop and validate a high-accuracy, high spatiotemporal resolution (2 km, daily), and multi-layer (0–0.1 m, 0.1–0.2 m, 0.2–0.5 m, 0.5–1.0 m) soil moisture dataset for the black soil region of China to support agricultural risk early warning systems and resource management.

Study Configuration

• Spatial Scale: Black soil region of China (approximately 1.24 million square kilometers), with a spatial resolution of 2 km.
• Temporal Scale: 16 years (2008–2023) at a daily scale, covering four soil depth layers: 0–0.1 m, 0.1–0.2 m, 0.2–0.5 m, and 0.5–1.0 m.

Methodology and Data

• Models used:
  • Community Land Model 3.5 (CLM3.5), enhanced with regional datasets.
  • Delta downscaling method for atmospheric forcing data.
• Data sources:
  • Input for CLM3.5:
    • Soil texture: Second National Soil Survey (SNSS) data, National Qinghai-Tibet Plateau Scientific Data Center.
    • Land cover: China Regional Land Cover Dataset (CLCV), MODIS (MCD12Q1.061).
    • Leaf Area Index (LAI): MODIS (MOD15A2H.061).
    • Atmospheric forcing: China Meteorological Administration Land Data Assimilation System (CMA-LDAS) scheme, ERA5-Land reanalysis (ECMWF), corrected with observational datasets from the National Qinghai-Tibet Plateau Science Data Center (1 km spatial resolution, monthly, 1961–2014).
  • Validation data: In-situ soil moisture measurements from seven field monitoring sites within the Northeast Black Soil Region of China.
  • Comparison data: ERA5-Land reanalysis dataset (ECMWF) and Global Land Data Assimilation System (GLDAS) soil moisture products.

Main Results

• A 16-year (2008–2023) daily multi-layer soil moisture dataset (DMSM) was generated for the black soil region of China, with a spatial resolution of 2 km and four depth layers (0–0.1 m, 0.1–0.2 m, 0.2–0.5 m, 0.5–1.0 m).
• Validation against seven field observation sites showed excellent performance:
  • Correlation coefficient (R) was generally greater than 0.7.
  • Root Mean Square Error (RMSE) varied between 0.035 and 0.07 (m³/m³).
  • Mean Absolute Error (MAE) ranged from 0.03 to 0.06 (m³/m³).
  • Bias remained between -0.02 and 0.02 (m³/m³).
• The DMSM effectively captured daily, inter-annual, and seasonal soil moisture variations, demonstrating good consistency with precipitation events.
• Spatial correlation analysis showed high consistency (R > 0.7 in most areas) between DMSM and both ERA5-SM and GLDAS-SM.
• Analysis of soil moisture trends from 2008 to 2023 indicated an increasing trend in most areas of the northeastern black soil region, particularly in the central and eastern parts at 0–0.1 m depth, suggesting a trend towards wetter conditions. Decreasing trends were observed in the southwestern sandy soil areas.

Contributions

• Provides a novel, high-accuracy, high spatiotemporal resolution (2 km, daily), and multi-layer (0–1.0 m) soil moisture dataset (DMSM) for the black soil region of China, addressing the limitations of existing datasets in simultaneously achieving these characteristics.
• Enhances the Community Land Model 3.5 (CLM3.5) framework by integrating high-resolution regional datasets (Second National Soil Survey soil texture, China Regional Land Cover Dataset land cover) and atmospheric forcing from the China Meteorological Administration Land Data Assimilation System (CMA-LDAS) for improved regional soil moisture simulation.
• Offers a crucial dataset for understanding the mechanisms of drought and waterlogging, predicting their impact on crop yields, and guiding farmland management in a globally significant grain production base.

Funding

• Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences (No.XDA28020501)
• Science and technology research project of Education Department of Jilin Province (938038)
• Key Research and Development Project of Jilin Province (20240304135SF)
• Joint Research Project on Improving Meteorological Capability of China Meteorological Administration (23NLTSQ008)
• Songliao Basin Meteorological Science and Technology Innovation Project (SL202401)
• Northeast regional science and technology collaborative innovation joint fund project (2024ZD004)

Citation

@article{Chen2025Generating,
  author = {Chen, Liwen and Wang, Yangguang and Ren, Jingquan and Song, Haiqing and Zhang, Guangxin and Sun, Jingxuan and Wang, Lijun and Mu, Jia and Liu, Cong and Wang, Mei-Yu},
  title = {Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-05986-7},
  url = {https://doi.org/10.1038/s41597-025-05986-7}
}