| 星载 GNSS-R 海面风速反演方法对比与性能评估 |
| Comparative Study and Performance Assessment of Spaceborne CNSS-R Sea SurfaceWind Speed Retrieval Methods |
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| DOI：10.3969/j.issn.1003-2029.2025.06.002 |
| 中文关键词: 全球导航卫星系统反射 海面风速反演 地球物理模型函数 最小方差估计方法 粒子群优化方法 |
| 英文关键词:Global Navigation Satellite System Reflectometry ocean wind speed retrieval Geophysical Modeling Functions Minimum Variance Estimation method Particle Swarm Optimization method |
| 基金项目:中央高校基本科研业务费专项资金资助项目（24CX02030A） |
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| 中文摘要: |
| 利用全球导航卫星系统反射(Global Navigation Satellite System Reflectometry，GNSS-R)信号反演海面风速时，首先从时延-多普勒图(Delay-Doppler Map，DDM) 中提取归一化双基雷达散射截面 (Normalized Bistatic Radar Cross Section, NBRCS) 和前沿斜率(Leading Edge Slope,LES)，并分别构建与海面风速相关的地球物理模型函数(Geophysical Model Functions，GMF)，然后采用最小方差估计(Minimum Variance Estimator，MVE) 方法和粒子群优化(Particle SwarmOptimization，PSO) 方法对 NBRCS GMF和LESGMF反演结果进行组合，以期获得优于单一观测值反演精度的结果。对NBRCSGMF、LESGMF、MVE方法和 PSO方法的风速反演结果进行对比分析，结果显示MVE和PSO两种组合反演方法整体均方根误差和相关系数均为1.79m/s和0.75，但PSO方法的系统偏差略大于MVE方法;相较于NBRCS GMF和IESGMF，两种组合反演方法均方根误差分别降低了约53%和16.0%。结果表明：融合多个观测值的组合反演方法优于传统单一观测值GMF方法，能够提高GNSS-R风速反演的可靠性与反演精度。 |
| 英文摘要: |
| When retrieving sea surface wind speed using Global Navigation Satellite System Reflectometry (GNSS-R) signals, the Normalized Bistatic Radar Cross Section (NBRCS) and Leading Edge Slope (LES) are first extracted from the Delay-Doppler Map (DDM). Subsequently, corresponding Geophysical Model Functions (GMFs) relating them to sea surface wind speed are established respectively. Then, the Minimum Variance Estimator (MVE) and Particle Swarm Optimization (PSO) methods are employed to combine the wind speed retrieval results from the NBRCS GMF and LES GMF, with the aim of achieving better accuracy than that obtained from a single observation.A comparative analysis of the wind speed retrieval results from the NBRCS GMF, LES GMF, MVE method, and PSO method shows that both the MVE and PSO combined retrieval methods achieve an overall root mean square error (RMSE) of 1.79 m/s and a correlation coefficient of 0.75. However, the systematic bias of the PSO method is slightly larger than that of the MVE method. Compared to the NBRCS GMF and LES GMF, the two combined retrieval methods reduce the RMSE by approximately 5.3% and 16.0%, respectively.The results indicate that the combined retrieval method, which fuses multiple observations, is superior to the traditional single-observation GMF method, and it can enhance both the reliability and the accuracy of GNSS-R wind speed retrieval. |
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