Lv et al. (2025) Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-27
• Authors: Shaoning Lv, Edward Ayres, Yin Hu
• DOI: 10.3390/rs17233845

Research Groups

• Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
• Zhuhai Fudan Innovation Research Institute, Zhuhai, China
• Shanghai Key Laboratory of Ocean-Land-Atmosphere Boundary Dynamics and Climate Change, Shanghai, China
• Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate, Ministry of Education, Fudan University, Shanghai, China
• National Ecological Observatory Network (NEON), Battelle, Boulder, CO, USA

Short Summary

This study validates the τ-z model's ability to estimate soil temperature sensing depth (zTeff) using high-temporal resolution data from the NEON and SMAP missions. It demonstrates the model's high accuracy, especially under monotonic soil conditions, enhancing confidence in passive microwave remote sensing for soil moisture and temperature retrieval across diverse ecosystems.

Objective

• To comprehensively validate the τ-z model for estimating soil temperature sensing depth (zTeff) across diverse ecosystems by harmonizing ground-based data with SMAP satellite observations, addressing a critical gap in multi-scale model evaluation under varying environmental conditions.

Study Configuration

• Spatial Scale: 40 terrestrial NEON sites across the continental United States; SMAP 36 km grid cells; NEON point measurements (0–5 cm depth) aggregated to 1 km × 1 km grids within a 3 km × 3 km buffer zone; soil temperature and moisture profiles up to 2 m depth with up to 9 sensor depths per site.
• Temporal Scale: High-temporal resolution data from July 2019 to June 2022 (over 36 months); NEON data collected at 15 min intervals, matched to SMAP's 3-day revisit cycle by selecting the median value within ±12 hours of SMAP overpass times.

Methodology and Data

• Models used: τ-z model, radiation transfer model (for effective temperature, Teff), Dobson (1985) dielectric model (for soil dielectric properties), Mironov (2009) dielectric model (mentioned for L-band frozen soils).
• Data sources: Soil Moisture Active Passive (SMAP) satellite mission (L-band brightness temperature, soil moisture data); National Ecological Observatory Network (NEON) (high-temporal resolution ground-based soil temperature, soil moisture, and soil texture data from 40 terrestrial sites).

Main Results

• The τ-z model accurately estimates soil temperature sensing depth (zTeff), with optimal performance around 0.2 τ under monotonic soil conditions.
• Combining SMAP soil moisture data, the τ-z model achieves high accuracy in estimating zTeff, with a Root Mean Square Difference (RMSD) of 0.05 m, an unbiased RMSD (unRMSD) of 0.03 m, and a correlation coefficient (r) of 0.67 between estimated and observed values.
• For soil profiles strictly adhering to the monotonic assumption, the model shows robust performance with RMSD and unRMSD stabilizing at 0.11 τ, and the correlation coefficient improving to 0.69.
• Under complex scenarios like frozen soils or transiently thawed layers, the model maintains acceptable accuracy, with RMSD values approximately 0.2 τ.
• The model's applicability is limited in "frozen" and "unfrozen & nonmonotonic" conditions due to irregular scatter distribution and Teffnor falling outside the valid range [0, 1].

Contributions

• Demonstrates the robustness of the τ-z model across a broader spectrum of soil conditions (monotonic, frozen, nonmonotonic profiles) and spatial scales, leveraging high-temporal resolution data from NEON and SMAP.
• Enhances confidence in the retrieval of soil moisture and temperature from passive microwave remote sensing, providing a solid foundation for applications in agriculture, hydrology, and climate change monitoring.
• Achieves a marked improvement in zTeff estimation (RMSD of 0.05 m, unRMSD of 0.1 m) by explicitly accounting for soil state transitions, outperforming previous models that typically exhibited errors greater than 0.3 m.
• Advances the operational applicability of L-band remote sensing in global ecological and hydrological research, particularly for global soil moisture validation in regions with limited in situ measurements.

Funding

• Key Research and Development and Achievement Transformation Program of Inner Mongolia Autonomous Region, China (Grant No. 2025YFDZ0007)
• Yan Liyuan–ENSKY Foundation Project of Zhuhai Fudan Innovation Research Institute (Grant No. JX240002)
• National Key R&D Program of China (Grant No. 2022YFF0801404)
• National Natural Science Foundation of China (Grant No. 42075150)
• US National Science Foundation (through the NEON Program)

Citation

@article{Lv2025Validation,
  author = {Lv, Shaoning and Ayres, Edward and Hu, Yin},
  title = {Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17233845},
  url = {https://doi.org/10.3390/rs17233845}
}