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Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data

Authors :
Zhichao Zheng
Qiushuang Yan
Chenqing Fan
Junmin Meng
Jie Zhang
Tianran Song
Weifu Sun
Source :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10938-10955 (2024)
Publication Year :
2024
Publisher :
IEEE, 2024.

Abstract

This study presents an innovative approach for estimating the azimuth cutoff wavelength ($\lambda _{c}$) using a multipolarization combination technique to enhance the retrieval of significant wave height (SWH) and wind speed (WS) from Gaofen-3 (GF-3) SAR wave mode data. The study identifies distinct advantages of copolarization for low to moderate sea states and cross-polarization for high sea states in the $\lambda _{c}$ estimation. Consequently, a suite of dual and quad-polarization combination methods is proposed, with the VV+VH combination demonstrating superior cost-efficiency, reducing the root mean square error (RMSE) of $\lambda _{c}$ estimation by around 20% compared with VV polarization. Correlation analysis between $\lambda _{c}$ at various polarizations, particularly VV+VH, and factors such as SWH, WS, wind direction, wave direction, and incidence angle, indicates a strong positive relationship with SWH and WS, and a moderate relationship with incidence angle. This insight informs the development of three $\lambda _{c}$-based SWH and WS retrieval models: single linear regression, multiple linear regression (MLR), and Gaussian process regression (GPR). The MLR and GPR models integrate normalized radar cross section (NRCS) and incidence angle to improve retrieval accuracy. The GPR model achieves more accurate estimation of SWH and WS compared with existing $\lambda _{c}$-based algorithms, with an RMSE of 0.485 m for SWH retrieval and 1.390 m/s for WS retrieval. Despite the performance gap with state-of-the-art algorithms, the GPR model offers exceptional cost-effectiveness and surpasses NRCS-based models for WS retrieval without requiring wind direction input.

Details

Language :
English
ISSN :
19391404 and 21511535
Volume :
17
Database :
Directory of Open Access Journals
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.6c5f7d7c32624bf69491b8a794b46068
Document Type :
article
Full Text :
https://doi.org/10.1109/JSTARS.2024.3405736