Sea wind retrieval by analytically-based geophysical model functions and sentinel-1A SAR images

被引:0
|
作者
Radkani N. [1 ]
Zakeri B. [1 ]
机构
[1] Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol
关键词
Composite models - Geophysical model function - Normalized radar cross section - Rough surface scattering - Sea wind retrieval - Sea-surface wind speed - Small slope approximation - Synthetic aperture radar (SAR) images;
D O I
10.2528/pierc19032705
中图分类号
学科分类号
摘要
In this paper, the sea surface wind speeds are retrieved by using an analytical scattering model, so called the analytically-based geophysical model function (GMF), from C-band Sentinel-1A V V-polarized synthetic aperture radar (SAR) images. The analytical models accurately simulate the rough surface scattering in the incidence angles range of SARs. The accuracy of the scattering results of the models depends on the sea wave spectrum. In this work, the effect of the sea spectral models on the accuracy of the sea surface wind speed retrieving is evaluated. In this regard, for omnidirectional and directional parts of sea spectrum, the Elfouhaily/Hwang spectra and Elfouhaily/McDaniel’s models are employed, respectively. The V V-polarized backscattered normalized radar cross-section (NRCS) is calculated by using the first-order small-slope approximation (SSA1) with the four composite models of the mentioned omnidirectional spectra and angular spreading functions (directional part), and the backscattering results are compared with the empirical model CMOD6. Then, from the V V-polarized Sentinel-1A SAR data in two resolutions, the wind speeds are estimated by the analytical and empirical models. The comparison of analytical models with CMOD6 shows that Hwang-Elfouhaily model is the best among the composite models. The results show that the analytical scattering models can be easily used for the sea wind speed retrieving below 20 m/s. © 2019, Electromagnetics Academy. All rights reserved.
引用
收藏
页码:223 / 236
页数:13
相关论文
共 50 条
  • [41] Developing a Subswath-Based Wind Speed Retrieval Model for Sentinel-1 VH-Polarized SAR Data Over the Ocean Surface
    Zhang, Kangyu
    Huang, Jingfeng
    Mansaray, Lamin R.
    Guo, Qiaoying
    Wang, Xiuzhen
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (03): : 1561 - 1572
  • [42] A Wavelet-Based Technique for Sea Wind Extraction from SAR Images
    Zecchetto, Stefano
    De Biasio, Francesco
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (10): : 2983 - 2989
  • [43] Precise identification of maize in the North China Plain based on Sentinel-1A SAR time series data
    Li, Li
    Kong, Qingling
    Wang, Pengxin
    Xun, Lan
    Wang, Lei
    Xu, Lianxiang
    Zhao, Zuliang
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (5-6) : 1996 - 2013
  • [44] Estimation of High-resolution Sea Wind in Coastal Areas Using Sentinel-1 SAR Images with Artificial Intelligence Technique
    Joh, Sung-uk
    Ahn, Jihye
    Lee, Yangwon
    KOREAN JOURNAL OF REMOTE SENSING, 2021, 37 (05) : 1187 - 1198
  • [45] Sea surface wind retrieval in coastal areas by means of Sentinel-1 and numerical weather prediction model data
    Rana, Fabio Michele
    Adamo, Maria
    Lucas, Richard
    Blonda, Palma
    REMOTE SENSING OF ENVIRONMENT, 2019, 225 : 379 - 391
  • [46] AUTOMATED GEOPHYSICAL CLASSIFICATION OF SENTINEL-1WAVE MODE SAR IMAGES THROUGH DEEP-LEARNING
    Wang, Chen
    Mouche, Alexis
    Tandeo, Pierre
    Stopa, Justin
    Chapron, Bertrand
    Foster, Ralph
    Vandemark, Douglas
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 1776 - 1779
  • [47] Spatial Scale Effect on Wind Speed Retrieval Accuracy Using Sentinel-1 Copolarization SAR
    Zhang, Kangyu
    Huang, Jingfeng
    Xu, Xiazhen
    Guo, Qiaoying
    Chen, Yaoliang
    Mansaray, Lamin R.
    Li, Zhengquan
    Wang, Xiuzhen
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (06) : 882 - 886
  • [48] Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network
    de Gelis, Iris
    Colin, Aurelien
    Longepe, Nicolas
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 5831 - 5841
  • [49] Dynamical System Approach for Wet Snow Retrieval in Mountains Using Sentinel-1 SAR Images
    James, Guillaume
    Karbou, Fatima
    Durand, Philippe
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 1
  • [50] Arctic Wintertime Sea Ice Lead Detection From Sentinel-1 SAR Images
    Chen, Shiyi
    Shokr, Mohammed
    Zhang, Lu
    Zhang, Zhilun
    Hui, Fengming
    Cheng, Xiao
    Qin, Peng
    Murashkin, Dmitrii
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62