A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery

2017 ◽  
Vol 55 (3) ◽  
pp. 1743-1751 ◽  
Author(s):  
Guosheng Zhang ◽  
William Perrie ◽  
Xiaofeng Li ◽  
Jun A. Zhang
Keyword(s):  
Surface Wind ◽  
Sea Surface ◽  
Wind Vector ◽  
Cross Polarization ◽  
Estimation Model ◽  
Model Based ◽  
Sea Surface Wind ◽  
Vector Estimation ◽  
Sar Imagery

A Study of Sea Surface Wind Vector Estimation From Near-Nadiral Cross-Track-Scanned Backscatter Data

2013 ◽  
Vol 10 (6) ◽  
pp. 1503-1506 ◽  
Author(s):  
Alexey Nekrasov ◽  
Jeff Ouellette ◽  
Ninoslav Majurec ◽  
Joel T. Johnson
Keyword(s):  
Surface Wind ◽  
Sea Surface ◽  
Wind Vector ◽  
Sea Surface Wind ◽  
Vector Estimation ◽  
Cross Track

scholarly journals FM-CW millimeter wave demonstrator system as a sensor of the sea surface wind vector

2004 ◽  
Vol 1 (6) ◽  
pp. 137-143 ◽  
Author(s):  
Alexey Nekrasov ◽  
Jacco J.M. de Wit ◽  
Peter Hoogeboom
Keyword(s):  
Millimeter Wave ◽  
Surface Wind ◽  
Sea Surface ◽  
Wind Vector ◽  
Sea Surface Wind

scholarly journals A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

2019 ◽  
Vol 11 (2) ◽  
pp. 153 ◽  
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Speed ◽  
Incidence Angle ◽  
Surface Wind ◽  
European Space Agency ◽  
Sea Surface ◽  
Cross Polarization ◽  
Retrieval Model ◽  
Sar Images ◽  
Wind Speeds ◽  
Sea Surface Wind

In contrast to co-polarization (VV or HH) synthetic aperture radar (SAR) images, cross-polarization (CP for VH or HV) SAR images can be used to retrieve sea surface wind speeds larger than 20 m/s without knowing the wind directions. In this paper, a new wind speed retrieval model is proposed for European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath (EW) mode VH-polarized images. Nineteen S-1A images under tropical cyclone condition observed in the 2016 hurricane season and the matching data from the Soil Moisture Active Passive (SMAP) radiometer are collected and divided into two datasets. The relationships between normalized radar cross-section (NRCS), sea surface wind speed, wind direction and radar incidence angle are analyzed for each sub-band, and an empirical retrieval model is presented. To correct the large biases at the center and at the boundaries of each sub-band, a corrected model with an incidence angle factor is proposed. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by SMAP. The results suggest that the proposed model can be used to retrieve wind speeds up to 35 m/s for sub-bands 1 to 4 and 25 m/s for sub-band 5.

Fusion of sea surface wind vector data acquired by multi-source active and passive sensors in China sea

2017 ◽  
Vol 38 (23) ◽  
pp. 6477-6491
Author(s):  
Juhong Zou ◽  
Minsen Lin ◽  
Bin Zou ◽  
Maohua Guo ◽  
Songxue Cui
Keyword(s):  
Surface Wind ◽  
Sea Surface ◽  
Wind Vector ◽  
Vector Data ◽  
China Sea ◽  
Sea Surface Wind ◽  
Passive Sensors

scholarly journals ATTEMPTS TO USE A NEW SEA SURFACE WIND ESTIMATION MODEL TO THE STUDY OF THE DESIGN OFFSHORE WAVE

2016 ◽  
Vol 72 (2) ◽  
pp. I_283-I_288
Author(s):  
Katsuyuki SUZUYAMA ◽  
Hitoshi SHIMABUKURO ◽  
Tetsu NAKAMURA ◽  
Toshiyuki KAWAMITSU ◽  
Hiroyuki KATO ◽  
...  
Keyword(s):  
Surface Wind ◽  
Sea Surface ◽  
Estimation Model ◽  
Wind Estimation ◽  
Sea Surface Wind ◽  
Offshore Wave
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