Sentinel-1 Noise Suppression Algorithm for High-Wind-Speed Retrieval in Tropical Cyclones - Dechen Ge; Lihua Wang; Weiwei Sun; Hongmei Wang; Wenjing Jiang; Tian Feng (Remote Sensing)
Sentinel-1 cross-polarization (cross-pol) SAR data, known for their unsaturated backscattering characteristics, hold strong potential for high-wind-speed retrieval in tropical cyclones (TCs). However, significant inherent noise in cross-pol data limits retrieval accuracy, especially under moderate-to-high wind conditions. Existing noise suppression methods remain insufficient due to their limited consideration of spatially varying noise characteristics within different TC structural regions. To address these challenges, this study proposes an enhanced two-dimensional noise field reconstruction framework based on Bayesian estimation, tailored to the structural features of TCs. The method begins by statistically characterizing cross-pol SAR backscatter to differentiate structural regions within TCs. Noise-scaling coefficients are then calculated to suppress scalloping artifacts, followed by the computation of power balance coefficients in sub-swath transition zones to mitigate abrupt inter-strip power variations through signal power equalization. Comparative assessments against the European Space Agency (ESA) noise vectors show that the proposed approach achieves an average signal-to-noise ratio (SNR) improvement of 2.54 dB. Subsequent sea surface wind speed retrievals using the denoised cross-pol data exhibit significant improvements: wind speed bias is reduced from −2.69 m/s to 0.65 m/s, accuracy is improved by 2.04 m/s, and the coefficient of determination (R2) increases to 0.88. These findings confirm the effectiveness of the proposed method in enhancing SAR-based wind speed retrieval under complex marine conditions associated with tropical cyclones.