Sea Surface Roughness Estimation Using a UAV

doi:10.1109/LGRS.2025.3628993

Sea Surface Roughness Estimation Using a UAV

2025-01-01

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DOI：10.1109/LGRS.2025.3628993

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This letter investigates the estimation of sea surface roughness using the vertical electromagnetic (EM) field obtained by an uncrewed aerial vehicle (UAV). A two-channel software-defined radio (SDR)-based receiver (RX) payload was used to measure the vertical EM field. Surface roughness affects the strength of the surface reflection, creating nulls and peaks at various altitudes caused by constructive and destructive interference between direct and surface-reflected fields. Simulated EM fields were created using a spherical Earth model with a roughness reduction factor from the Miller–Brown rough surface model. Significant wave height ( $H_{s}$ ) is estimated using the measured peak-to-null ratio. The accuracy of the drone-based inversion algorithm was tested during the Coastal Land–Air–Sea Interaction (CLASI) Campaign in Monterey, CA, USA, from June to September 2021. An EM RX payload was designed using a custom-built low-noise amplifier-limiter circuit and an SDR. The system was deployed from a large Aero Systems West Hexacopter with a 20 kg maximum payload capacity. Simultaneously, $H_{s}$ is measured using wave wire data from multiple buoys. The drone-based inversion method was able to estimate $H_{s}$ with 8% root mean square (rms) error in CLASI. The effects of lower atmospheric ducting conditions on $H_{s}$ estimation are also investigated.

Keywords:

Atmospheric ducting

Coastal Land–Air–Sea Interaction (CLASI)

electromagnetic (EM) propagation

lower atmospheric propagation

remote sensing

significant wave height

surface roughness

uncrewed aerial vehicle (UAV)

Sea Surface Roughness Estimation Using a UAV

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