A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR
A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR
复制标题
C波段SAR海面风速反演的神经网络方法
DOI：
10.3390/rs14092269
复制
发表时间：
2022
期刊：
影响因子：
5
通讯作者：
Wenfang Lu
中科院分区：
文献类型：
--
作者：
Peng Yu;Wenxiang Xu;Xiaojing Zhong;Johnny A. Johannessen;Xiao-Hai Yan;Xupu Geng;Yuanrong He;Wenfang Lu
提出了一种基于海洋投影和可拓神经网络（OPEN）的C波段合成孔径雷达（SAR）海面风速反演方法。为了证明一个强大的数据集的方法，五年归一化雷达截面（NRCS）测量先进的散射仪（ASCAT），一个著名的侧视雷达传感器，用于训练模型。现场风数据直接浮标观测，而不是再分析风数据或模式的结果，被用作地面真理在开放模式。该模型适用于检索海面风从两个独立的数据集，ASCAT和哨兵-1 SAR数据，并已得到很好的验证，使用浮标测量从美国国家海洋和大气管理局（NOAA）和中国气象局（CMA），和ASCAT沿海风产品。OPEN模型和四个C波段模型（CMOD）版本（CMOD 4、CMOD-IFR 2、CMOD 5. N和CMOD 7）之间的比较进一步表明了所提出的C波段SAR传感器模型的良好性能。预计使用高分辨率合成孔径雷达数据和新的风速反演方法，将来可以提供连续和准确的海洋风产品。
Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future.