GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar
Luke Jacobs, Ishfaq Aziz, Benhao Lu, Alireza Tabatabaeenejad, Mohamad Alipour, Elahe Soltanaghai

TL;DR
GreenScatter is a physics-based framework that enables accurate UAV-based soil moisture sensing through canopies by modeling electromagnetic interactions and calibrating radar signals.
Contribution
It introduces a novel microwave radiative transfer model and RCS estimation method for improved soil moisture retrieval under vegetation cover.
Findings
Achieved an average VWC error of 4.49% in field tests.
Effectively isolates soil reflections from vegetation effects.
Robustly estimates soil moisture across different crop types and UAV configurations.
Abstract
Soil moisture is a critical variable for managing irrigation, improving crop yield, and understanding field-scale hydrology. Radars mounted on unmanned aerial vehicles (UAVs) offer a promising means to monitor soil moisture over large fields with flexible, high-resolution coverage. However, during the growing season, canopy scattering and soil reflections become strongly coupled in the radar measurement. These coupled effects vary with crop structure or flight altitude, complicating the retrieval of soil moisture. To overcome this challenge, we present GreenScatter, a physics-based soil moisture retrieval framework for nadir-looking wideband UAV radars. GreenScatter introduces a microwave radiative transfer model that explicitly captures the dominant electromagnetic interactions between vegetation and soil, enabling accurate modeling of coherent ground backscatter through canopy. In…
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