GNSS Reflectometry Global Ocean Wind Speed Estimation Based on CyGNSSMsa

GNSS Reflectometry (GNSS-R) technology utilizes existing navigation satellite signals as opportunistic microwave illumination sources, eliminating the need for dedicated transmitters and thereby significantly reducing hardware costs. Benefiting from the global coverage of navigation constellations, GNSS-R enables worldwide sea surface wind monitoring with superior spatiotemporal resolution. The acquired Delay-Doppler Maps (DDMs) encode sea surface roughness characteristics near the specular reflection point, which are intrinsically modulated by wind-driven capillary waves. We proposed CyGNSSMsa employs CyGNSSnet’s dual-branch framework for preliminary feature extraction from DDM and auxiliary parameters, followed by multihead self-attention (MSA) mechanisms to decipher long-range dependencies within fused feature representations. Multi-scale feature fusion is achieved through residual connections across hierarchical network layers, culminating in precise wind speed regression via multilayer perceptrons. Experimental validation demonstrates CyGNSSMsa’s performance with an RMSE of 1.345 m/s, achieving simultaneous improvements in both accuracy and systematic bias mitigation.

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Publication Details

Journal
Journal of Computer and Communications
Published
2025-01-01
DOI
https://doi.org/10.4236/jcc.2025.136013
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

GNSS Reflectometry Global Ocean Wind Speed Estimation Based on CyGNSSMsa

Dongmei Song, Weimin Chen, Bin Wang
1 links
Journal of Computer and Communications
Soil Moisture and Remote Sensing
article

GNSS Reflectometry Global Ocean Wind Speed Estimation Based on CyGNSSMsa

Dongmei Song, Weimin Chen, Bin Wang
article en

Abstract

GNSS Reflectometry (GNSS-R) technology utilizes existing navigation satellite signals as opportunistic microwave illumination sources, eliminating the need for dedicated transmitters and thereby significantly reducing hardware costs. Benefiting from the global coverage of navigation constellations, GNSS-R enables worldwide sea surface wind monitoring with superior spatiotemporal resolution. The acquired Delay-Doppler Maps (DDMs) encode sea surface roughness characteristics near the specular reflection point, which are intrinsically modulated by wind-driven capillary waves. We proposed CyGNSSMsa employs CyGNSSnet’s dual-branch framework for preliminary feature extraction from DDM and auxiliary parameters, followed by multihead self-attention (MSA) mechanisms to decipher long-range dependencies within fused feature representations. Multi-scale feature fusion is achieved through residual connections across hierarchical network layers, culminating in precise wind speed regression via multilayer perceptrons. Experimental validation demonstrates CyGNSSMsa’s performance with an RMSE of 1.345 m/s, achieving simultaneous improvements in both accuracy and systematic bias mitigation.

Journal of Computer and CommunicationsVol. 13(06)
National Aeronautics and Space Administration, National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Key Technology Research and Development Program of Shandong, Fundamental Research Funds for the Central Universities
Life below water
Openalex Percentile: Top 86%
Soil Moisture and Remote Sensing
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