FN Archimer Export Format
PT J
TI Significant wave heights from Sentinel-1 SAR: Validation and applications
BT 
AF STOPA, Justin
   MOUCHE, Alexis
AS 1:1;2:1,2;
FF 1:PDG-ODE-LOPS-SIAM;2:PDG-ODE-LOPS-SIAM;
C1 Univ Brest, CNRS, IFREMER, IRD,IUEM,LOPS, Brest, France.
   Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Jiangsu, Peoples R China.
C2 IFREMER, FRANCE
   UNIV NANJING, CHINA
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   copubli-int-hors-europe
   copubli-sud
IF 2.711
TC 78
UR https://archimer.ifremer.fr/doc/00363/47415/47415.pdf
   https://archimer.ifremer.fr/doc/00363/47415/47417.pdf
   https://archimer.ifremer.fr/doc/00363/47415/47418.csv
   https://archimer.ifremer.fr/doc/00363/47415/47419.csv
   https://archimer.ifremer.fr/doc/00363/47415/47420.csv
   https://archimer.ifremer.fr/doc/00363/47415/47421.csv
LA English
DT Article
AB Two empirical algorithms are developed for wave mode images measured from the synthetic aperture radar aboard Sentinel-1 A. The first method, called CWAVE_S1A, is an extension of previous efforts developed for ERS2 and the second method, called Fnn, uses the azimuth cutoff amongst other parameters to estimate significant wave heights and average wave periods without using a modulation transfer function. Neural networks are trained using co-located data generated from WAVEWATCH III and independently verified with data from altimeters and in-situ buoys. We use neural networks to relate the nonlinear relationships between the input SAR image parameters and output geophysical wave parameters. CWAVE_S1A performs well and has reduced precision compared to Fnn with Hs root mean square errors within 0.5 and 0.6 m respectively. The developed neural networks extend the SAR's ability to retrieve useful wave information under a large range of environmental conditions including extra-tropical and tropical cyclones. 
PY 2017
PD MAR
SO Journal Of Geophysical Research-oceans
SN 2169-9275
PU Amer Geophysical Union
VL 122
IS 3
UT 000400678900012
BP 1827
EP 1848
DI 10.1002/2016JC012364
ID 47415
ER

EF