Qi et al. (2025) Exploring How Soil Moisture Varies with Soil Depth in the Root Zone and Its Rainfall Lag Effect in the Ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-29
• Authors: Yue Qi, Siyu Wang, Jun Ma, Kexin Lv, Syed Moazzam Nizami, Chunhong Zhao, Qun’ou Jiang, Jiankun Huang
• DOI: 10.3390/rs18010120

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study develops and validates a multi-depth soil moisture retrieval model for the Qinghai–Tibetan Plateau to Loess Plateau ecotone using multi-source remote sensing and SVAT/TSEB models, revealing complex temporal and spatial soil moisture dynamics and a pronounced rainfall lag effect with increasing depth.

Objective

• To construct a retrieval model for soil moisture at various depth layers using multi-source remote sensing data, the two-source energy balance (TSEB) model, and the soil–vegetation–atmosphere transfer (SVAT) model.
• To uncover how soil moisture changes across various depths in the root zone within the study area.
• To discuss the lagging effect of rainfall on soil moisture at different depths.

Study Configuration

• Spatial Scale: Ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau (QPtoLP).
• Temporal Scale: Growing season (mid-May to mid-August), with observations of a July dry spell and a rainfall lag effect of up to three days.

Methodology and Data

• Models used: Two-source energy balance (TSEB) model, Soil–vegetation–atmosphere transfer (SVAT) model.
• Data sources: Multi-source remote sensing data, field-monitored soil moisture values (for validation).

Main Results

• The constructed retrieval model showed a strong correlation (0.720 to 0.8414) between retrieved and field-monitored soil moisture values across various depth layers, indicating its suitability for the study area.
• During the growing season, soil moisture slightly decreased from mid-May to mid-June, partially recovered in mid-June, reached its lowest point after a July dry spell, and then rebounded by mid-August (surface soil moisture above 0.2 m³/m³, deep soil moisture above 0.1 m³/m³).
• Spatially, soil moisture was higher in the southern region (dense human activities) and lower in the northern region (alpine grasslands).
• Soil moisture at 0–0.05 m depth was generally the highest, except in July when the 0.35–0.50 m depth had the highest value.
• Surface soil moisture at 0–0.05 m depth exhibited frequent fluctuations at elevations above 4000 m.
• The rainfall lag effect became more pronounced with increasing soil depth, with a 3-day lag observed in the 0.35–0.50 m soil layer.

Contributions

• First-time construction and validation of a multi-depth soil moisture retrieval model for the specific QPtoLP ecotone, integrating multi-source remote sensing with TSEB and SVAT models.
• Provides detailed insights into the temporal and spatial dynamics of soil moisture across various root zone depths in this critical ecotone.
• Quantifies the rainfall lag effect at different soil depths, highlighting its increasing prominence with depth in the study area.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Qi2025Exploring,
  author = {Qi, Yue and Wang, Siyu and Ma, Jun and Lv, Kexin and Nizami, Syed Moazzam and Zhao, Chunhong and Jiang, Qun’ou and Huang, Jiankun},
  title = {Exploring How Soil Moisture Varies with Soil Depth in the Root Zone and Its Rainfall Lag Effect in the Ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs18010120},
  url = {https://doi.org/10.3390/rs18010120}
}