FY-3E WindRAD Data Enhancement Method Based on Improved Adaptive Bilateral Total Variation Regularization Algorithm and Multipass Reconstruction Strategy

• Published in: IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 17
• Main Authors: Li, Lilan, Gu, Lingjia, Shang, Jian, Hu, Xiuqing, Ren, Ruizhi
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Antenna pattern; C-band; Image reconstruction; Image resolution; Ku-band; LABTV+ regularization reconstruction (RR) algorithm; multipass reconstruction strategy; Radar measurements; Satellites; scatterometer; Sea measurements; Signal resolution; Spaceborne radar; Spatial resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2025.3587134

Spaceborne scatterometers are active nonimaging radar systems, become one of the most effective sensors in the field of quantitative remote sensing for global observation. However, their nominal resolution of 25-50 km limits their applicability in scenarios requiring higher resolution. In this article, an improved adaptive bilateral-total-variation regularization reconstruction (RR) algorithm with Lorentzian norm (LABTV+ RR algorithm) is specifically designed for the world's first dual-frequency scatterometer, Fengyun-3E Wind Radar (FY-3E WindRAD) data. LABTV+ RR algorithm introduces dynamically adaptive regularization parameters based on our previously proposed LABTV RR algorithm, which effectively suppresses the noise while maintaining the image texture details, enabling more flexible adaptation to different image contents and noise levels. In addition, the performance of the LABTV+ RR algorithm is further optimized by employing Barzilai-Borwein (BB) stepsize, which dynamically adjusts the stepsize and accelerates the convergence speed of the solution. In this study, the effectiveness of the LABTV+ RR algorithm is validated by comparing actual data and simulated images. The spatial response function (SRF) derived from the actual antenna patterns is used to validate the algorithm's performance on FY-3E WindRAD Level 1B (L1B) data. Specifically, the algorithm is tested on C-band with a spatial resolution pixel size of <inline-formula> <tex-math notation="LaTeX">25\times 0.25 </tex-math></inline-formula> km and Ku-band data with a spatial resolution pixel size of <inline-formula> <tex-math notation="LaTeX">10\times 0.25 </tex-math></inline-formula> km. The validation includes both horizontally polarized (HH-pol) and vertically polarized (VV-pol) transmitted and received signals. In addition, two representative regions in China are chosen as the study regions to demonstrate the algorithm's capability to enhance resolution to 3.125 km in both C-band and Ku-band. Furthermore, a multi-pass reconstruction strategy is proposed to achieve an even higher resolution pixel size of 1.5625 km for FY-3E WindRAD C-band and Ku-band data. The study has demonstrated the effectiveness of the proposed LABTV+ RR algorithm in enhancing the resolution of FY-3E WindRAD data, as evidenced by both qualitative visual assessments and quantitative evaluation metrics.