Zheng et al. (2025) Modeling, prediction, and retrieval of surface soil moisture from InSAR closure phase

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-11-04
• Authors: Yujie Zheng, Heresh Fattahi
• DOI: 10.1016/j.rse.2025.115104

Research Groups

• Department of Sustainable Earth System Sciences, University of Texas at Dallas, USA
• Jet Propulsion Laboratory, California Institute of Technology, USA

Short Summary

This study presents a discretized, multi-layer soil moisture model that links soil moisture variability to single-look SAR measurements and their closure phase. It introduces a scalable algorithm for retrieving a relative InSAR Soil Moisture Index, demonstrating its potential for large-scale soil moisture monitoring through validation against in situ and satellite data.

Objective

• To develop a discretized, multi-layer soil moisture model that links soil moisture variability to single-look Synthetic Aperture Radar (SAR) measurements and their closure phase.
• To identify distinct closure phase signatures arising from variations in soil moisture, radar frequencies, and soil textures.
• To introduce and validate a scalable algorithm for retrieving the InSAR Soil Moisture Index, a relative soil moisture product, using InSAR closure phase.

Study Configuration

• Spatial Scale: Regional (Mojave Desert and Central Valley in California) with potential for large-scale monitoring at high resolution (tens of meters).
• Temporal Scale: Not explicitly defined for a specific study period, but the methodology involves analyzing variations in soil moisture over time using multiple SAR acquisitions for closure phase calculation.

Methodology and Data

• Models used: Discretized, multi-layer soil moisture model; approximate transfer function between soil moisture anomalies and closure phase responses.
• Data sources: Single-look SAR measurements (including L-band radar), InSAR closure phase, in situ soil moisture measurements, SMAP/Sentinel-1 soil moisture measurements.

Main Results

• The developed model reveals distinct closure phase signatures influenced by soil moisture variability, radar frequencies, and soil textures.
• Positive asymmetric soil moisture anomalies are predicted to produce positive closure phase step-changes, while negative asymmetric anomalies yield negative step-changes, consistent with observed data.
• Low-frequency radar (e.g., L-band) demonstrates heightened sensitivity to the vertical distribution of soil moisture.
• An approximate transfer function between soil moisture anomalies and closure phase responses was identified.
• A scalable algorithm for retrieving the InSAR Soil Moisture Index (a relative soil moisture product) was successfully introduced and demonstrated in two diverse environments (Mojave Desert and Central Valley, California).
• The derived InSAR Soil Moisture Index showed good agreement with both in situ soil moisture measurements and SMAP/Sentinel-1 soil moisture measurements.

Contributions

• Development of a novel discretized, multi-layer soil moisture model that directly links soil moisture variability to single-look SAR measurements and the InSAR closure phase.
• Identification and characterization of distinct closure phase signatures in response to soil moisture changes, radar frequencies, and soil textures.
• Demonstration of the enhanced sensitivity of low-frequency radar (L-band) to the vertical distribution of soil moisture.
• Introduction of a scalable algorithm for retrieving a relative InSAR Soil Moisture Index using closure phase, effectively mitigating the influence of ground deformation and atmospheric delays that challenge traditional InSAR phase methods.
• Validation of the retrieval algorithm in diverse environments, highlighting its potential for accurate, large-scale, and high-resolution soil moisture monitoring.

Funding

Not specified in the provided text.

Citation

@article{Zheng2025Modeling,
  author = {Zheng, Yujie and Fattahi, Heresh},
  title = {Modeling, prediction, and retrieval of surface soil moisture from InSAR closure phase},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115104},
  url = {https://doi.org/10.1016/j.rse.2025.115104}
}