Shi et al. (2025) Meter-level resolution surface soil moisture estimation over agricultural fields from time-series quad-pol SAR with constraints of coarse resolution CCI data products

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-10
• Authors: Hongtao Shi, Zhong Lu, Jinqi Zhao, Wensong Liu, Tianjie Zhao, Liujun Zhu, Fengkai Lang, Lingli Zhao
• DOI: 10.1016/j.agwat.2025.109856

Research Groups

• School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, China
• Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources, Wuhan, Hubei, China
• Laboratory of Target Microwave Properties, Deqing, Zhejiang, China
• State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, Hubei, China
• Sunyueqi Honors College, China University of Mining and Technology, Xuzhou, Jiangsu, China
• School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, Jiangsu, China
• Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, Beijing, China
• National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, China
• School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China

Short Summary

This study developed a novel time-series L-band quad-polarimetric SAR algorithm, constrained by coarse-resolution soil moisture products, to estimate meter-level surface soil moisture over agricultural fields. The method achieved high accuracy, with root mean square errors ranging from 0.03 to 0.08 cm³/cm³ and correlation coefficients between 0.5 and 0.86 across various crop types.

Objective

• To develop an enhanced time-series algorithm for meter-level resolution surface volumetric soil moisture (SSM) mapping over agricultural fields using L-band quad-polarimetric (quad-pol) Synthetic Aperture Radar (SAR) data.
• To address the under-determined nature of soil dielectric constant estimation by incorporating soil permittivity constraints derived from coarse-resolution microwave SSM products.
• To improve the robustness and accuracy of SSM retrieval by employing a two-component polarimetric target decomposition technique to minimize the influence of vegetation scattering and its temporal variability.

Study Configuration

• Spatial Scale: Meter-level resolution (4 m in azimuth, 6 m in range for raw UAVSAR; 5.0 m in range, 7.2 m in azimuth for processed SAR) for SSM mapping; 0.25° × 0.25° for coarse-resolution CCI soil moisture constraints.
• Temporal Scale: Time-series observations over one month (June 17 to July 17, 2012) with 14 UAVSAR acquisitions at approximately 2 to 3 day intervals. CCI data spanned June 1 to July 30, 2012.

Methodology and Data

• Models used:
  • Time-series alpha approximation model (for soil dielectric constant retrieval).
  • Two-component polarimetric target decomposition (X-Bragg surface scattering model and step-wise volume scattering model) for separating soil and vegetation scattering.
  • Empirical and semi-empirical dielectric mixing models (Topp et al., 1980; Dobson et al., 1985; Zhang-Zhao model) for converting dielectric constant to volumetric soil moisture.
  • Least-squares algorithm with boundary constraints for solving the under-determined system of equations.
• Data sources:
  • NASA’s L-band quad-pol UAVSAR datasets (HH, HV/VH, VV) from the SMAP Validation Experiment 2012 (SMAPVEX12) campaign in Manitoba, Canada.
  • European Space Agency (ESA) Climate Change Initiative (CCI) combined soil moisture data products (0.25° × 0.25° resolution).
  • In-situ volumetric soil moisture measurements (HydraProbe, 6 cm depth) from canola, corn, soybean, wheat, and winter wheat fields.
  • Crop biophysical parameters (area plant water content, total dry biomass).
  • Soil texture information (bulk density, clay content, sand content, silt content) from SoilGrids250 m.
  • Weather station data (precipitation, air temperature, soil temperature).

Main Results

• The proposed method achieved volumetric SSM retrieval accuracies with root mean square errors (RMSE) ranging from 0.03 to 0.08 cm³/cm³ and correlation coefficients (R) between 0.5 and 0.86 across canola, corn, soybean, wheat, and winter wheat fields.
• VV polarization generally provided higher retrieval accuracy compared to HH polarization for most crop types (e.g., for canola, RVV = 0.58, RHH = 0.52; RMSEVV = 0.06 cm³/cm³, RMSEHH = 0.07 cm³/cm³).
• The empirical Topp model, despite its simplicity and not requiring soil properties, yielded soil moisture estimates with accuracy comparable to the more complex Zhang-Zhao model.
• Time-series SSM estimates showed good consistency with temporal trends of in-situ measurements and accurately reflected changes due to precipitation events.
• The method demonstrated robustness across different crop types, with overall RMSEs ranging from 5.0% to 7.0%, indicating its potential for high-resolution soil moisture mapping in vegetated agricultural areas.
• The use of unified coarse-resolution CCI constraints effectively refined the spatial continuity of soil moisture estimates.

Contributions

• Developed a novel time-series SSM retrieval algorithm for field-scale mapping (meter-level resolution) using L-band quad-pol SAR data, specifically designed for vegetated agricultural areas.
• Introduced a critical enhancement to the alpha approximation model by integrating soil permittivity constraints derived from coarse-resolution ESA CCI soil moisture products, effectively mitigating the under-determined nature of dielectric constant estimation and improving inversion accuracy.
• Incorporated a two-component polarimetric target decomposition technique to robustly separate soil surface scattering from vegetation scattering, enabling accurate SSM retrieval without reliance on ancillary optical data or empirical fitting models.
• Demonstrated superior or comparable retrieval accuracy (RMSE 0.03-0.08 cm³/cm³) compared to existing methods using the same SMAPVEX12 dataset, while utilizing a shorter time-series window (four dates), which reduces computational cost.

Funding

• National Natural Science Foundation of China (Grant 42301412, 42404002, 41977220, 42371369, 62201232, 62471337)
• Open fund of Laboratory of Target Microwave Properties (Grant No. 2022-KFJJ-003)
• Open research fund program of LIESMARS (Grant No. 22R05)
• Natural Science Foundation of Jiangsu Province, China (Grants No. BK20241670)
• Fundamental Research Funds for the Central Universities (Grant No. 2025-KJJC-A03)
• Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources (Grant 2022NGCM04, 2023NGCM12)

Citation

@article{Shi2025Meterlevel,
  author = {Shi, Hongtao and Wu, Qing and Lu, Zhong and Zhao, Jinqi and Liu, Wensong and Zhao, Tianjie and Zhu, Liujun and Lang, Fengkai and Zhao, Lingli},
  title = {Meter-level resolution surface soil moisture estimation over agricultural fields from time-series quad-pol SAR with constraints of coarse resolution CCI data products},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109856},
  url = {https://doi.org/10.1016/j.agwat.2025.109856}
}