Jacobs et al. (2026) GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: arXiv (Cornell University)
• Year: 2026
• Date: 2026-04-10
• Authors: Luke Jacobs, I. Aziz, Benhao Lu, Alireza Tabatabaeenejad, Mohamad Alipour, Elahe Soltanaghai
• DOI: None

Research Groups

• Remote Sensing and Hydrology Research Groups
• Agricultural Engineering and Crop Science Departments
• Electrical Engineering or Microwave Remote Sensing Laboratories

Short Summary

This paper introduces GreenScatter, a physics-based framework for retrieving soil moisture using nadir-looking wideband UAV radars, specifically addressing the challenge of vegetation-soil electromagnetic coupling. The framework demonstrates consistent soil moisture estimation through vegetation with an average volumetric water content error of 4.49% in field experiments.

Objective

• To develop a robust, physics-based soil moisture retrieval framework (GreenScatter) for nadir-looking wideband UAV radars that accurately accounts for the strong coupling between canopy scattering and soil reflections during the growing season.

Study Configuration

• Spatial Scale: Large agricultural fields, specifically multiple corn and soybean sites.
• Temporal Scale: During the growing season, implying a period of several months covering crop development.

Methodology and Data

• Models used:
  • GreenScatter: A physics-based soil moisture retrieval framework.
  • Microwave radiative transfer model: Explicitly captures dominant electromagnetic interactions between vegetation and soil, modeling coherent ground backscatter through canopy.
  • Radar Cross-Section (RCS) estimation method: Transforms time-domain radar signals into calibrated wideband RCS spectra, isolating soil reflections and compensating for hardware/waveform effects.
• Data sources:
  • Radars mounted on Unmanned Aerial Vehicles (UAVs).
  • Field experiments conducted across multiple corn and soybean sites (implying ground truth measurements for validation, though not explicitly detailed).

Main Results

• GreenScatter enables robust soil moisture estimation through vegetation across varying canopy conditions and UAV configurations.
• The framework achieves consistent soil moisture retrieval with an average volumetric water content (VWC) error of 4.49% in field experiments across multiple corn and soybean sites.
• The introduced microwave radiative transfer model accurately captures coherent ground backscatter through the canopy.
• The developed RCS estimation method effectively isolates soil reflections from time-domain radar signals, compensating for hardware and waveform effects.

Contributions

• Introduction of GreenScatter, a novel physics-based soil moisture retrieval framework specifically designed for nadir-looking wideband UAV radars to overcome challenges posed by vegetation.
• Development of a new microwave radiative transfer model that explicitly and accurately captures the dominant electromagnetic interactions between vegetation and soil, enabling precise modeling of coherent ground backscatter through the canopy.
• Creation of an innovative radar cross-section (RCS) estimation method that transforms time-domain radar signals into calibrated wideband RCS spectra, effectively isolating soil reflections while compensating for hardware and waveform effects.
• Provides a robust solution for high-resolution soil moisture monitoring in vegetated agricultural fields, which was previously complicated by coupled canopy scattering and soil reflections.

Funding

Not specified in the provided text.

Citation

@article{Jacobs2026GreenScatter,
  author = {Jacobs, Luke and Aziz, I. and Lu, Benhao and Tabatabaeenejad, Alireza and Alipour, Mohamad and Soltanaghai, Elahe},
  title = {GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar},
  journal = {arXiv (Cornell University)},
  year = {2026},
  url = {https://openalex.org/W7154427648}
}