GNSS-R观测下的海面飓风风速反演

利用全球导航卫星系统在地球表面的反射信号（GNSS-R）进行海面风速反演已经被广泛研究并作为一种重要的遥感手段。目前，该L波段微波信号的相关功率已可以在多普勒频率和时延码片空间进行多普勒时延图像的成像。由于该图像的图像特征与海面粗糙度有较高的相关性，因此能够用来进行海面风场反演。然而，对于该遥感手段而言，其双基雷达前向散射截面（BRCS）理论上与海面粗糙度有更高的相关性，如同目前合成孔径雷达使用后向雷达散射截面而非相关功率。所以，本文通过改进已有的GNSS-R的双基雷达散射截面方程，代替相关功率在多普勒时延空间进行成像，得出了与海面粗糙度相关的双基雷达散射截面图像（BRCS map）。基于该图像，本文提出了三种与其形状特征相关的观测量，通过2005年Dennis飓风GNSS-R机载数据生成的16000多幅图像进行地球物理模式函数建模并与经典的一维时延波形匹配方法得出结果进行对比分析，得出更为精确的风速反演结果。

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems (GNSSs) have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds. The power of GNSS reflectometry (GNSS-R) signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps (DDMs), whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds. However, the bistatic radar cross section (BRCS), which is strongly related to the sea surface roughness, is extensively used in radar. Therefore, a bistatic radar cross section (BRCS) map with a modified BRCS equation in a GNSS-R application is introduced. On the BRCS map, three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed. Airborne Hurricane Dennis (2005) GNSS-R data are then used. More than 16 000 BRCS maps are generated to establish GMFs of the three observables. Finally, the proposed model and classic one-dimensional delay waveform (DW) matching methods are compared, and the proposed model demonstrates a better performance for the high wind speed retrievals.