Usman et al. (2025) Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-14
• Authors: Muhammad Usman, Christopher E. Ndehedehe
• DOI: 10.3390/rs17223723

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study developed and validated a HYDRUS-1D numerical model to estimate long-term soil moisture in the data-scarce Northern Territory, Australia, demonstrating good performance and identifying CHRS-CCS as the most effective remote sensing precipitation product for this application.

Objective

• To develop and validate a HYDRUS-1D numerical model for accurate, long-term soil moisture estimation in the data-scarce Northern Territory, Australia, utilizing remote sensing precipitation data.

Study Configuration

• Spatial Scale: Point-scale (1D vertical) model applied to a shrubland site within the Northern Territory, Australia, representative of a Tropical-Savannah climate.
• Temporal Scale: Calibration period: March 2012 to February 2013 (1 year). Validation periods: March 2013 to October 2016 (approximately 3.5 years across three distinct periods). Total study period: March 2012 to October 2016 (approximately 4.5 years).

Methodology and Data

• Models used: HYDRUS-1D, based on the numerical solution of Richards’ equation for variably saturated flow, utilizing optimized van Genuchten model parameters.
• Data sources: Limited in situ soil moisture measurements for model calibration and validation; remotely sensed precipitation data from CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now.

Main Results

• The HYDRUS-1D model exhibited good performance in simulating measured soil moisture:
  • Calibration: Root Mean Square Error (RMSE) = 0.00 m³/m³, Mean Absolute Error (MAE) = 0.005 m³/m³, Pearson’s correlation coefficient (R) = 0.70.
  • Validation Period 1: RMSE = 0.035 m³/m³, MAE = 0.023 m³/m³, R = 0.72.
  • Validation Period 2: RMSE = 0.054 m³/m³, MAE = 0.039 m³/m³, R = 0.51.
  • Validation Period 3: RMSE = 0.046 m³/m³, MAE = 0.032 m³/m³, R = 0.61.
• Remotely sensed precipitation products (CHRS-PERSIANN, CHRS-CCS, CHRS-PDIR-Now) generally underestimated soil moisture.
• CHRS-CCS outperformed other remote sensing products in overall efficiency, with an average RMSE of 0.040 m³/m³, average MAE of 0.023 m³/m³, and an average R of 0.68.

Contributions

• Developed and validated an integrated numerical modeling and remote sensing approach for long-term soil moisture estimation in a data-scarce region (Northern Territory, Australia).
• Provided a comprehensive uncertainty analysis for the calibration process of the HYDRUS-1D model.
• Assessed the capabilities of various remotely sensed precipitation products (CHRS-PERSIANN, CHRS-CCS, CHRS-PDIR-Now) for soil moisture estimation in a tropical savannah environment, identifying the best-performing product.
• Contributes to understanding long-term soil moisture dynamics and soil water balance in the region.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Usman2025Leveraging,
  author = {Usman, Muhammad and Ndehedehe, Christopher E.},
  title = {Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17223723},
  url = {https://doi.org/10.3390/rs17223723}
}