Wave Parameters Estimated from Scatterometer Data

Author

Guo, Jie ; He, Yijun ; Perrie, William

Volume

4

fYear

2008

fDate

7-11 July 2008

Abstract

A new model is proposed to estimate significant wave heights from ERS-1/2 scatterometer data. We find that the relationship between wave parameters and the radar backscattering cross section is similar to that between the radar backscattering cross section and wind. The model for the relation between significant wave height and the radar cross section is obtained by a neural network algorithm. When the average wave period is less than 7s, the root mean square of significant wave height retrieved from ERS-1/2 data is 0.51 m. When the average wave period is more than 7 s, it is 0.72 m.

Keywords

geophysics computing; neural nets; ocean waves; radar cross-sections; remote sensing by radar; wind; ERS-1/2 scatterometer data; neural network algorithm; ocean wave heights estimation; radar backscattering cross section; root mean square; sea surface wind vector; wave period; Ocean waves; Parameter estimation; Radar cross section; Radar measurements; Satellites; Sea measurements; Sea surface; Spaceborne radar; Spatial resolution; Wind speed; Scatterometer; neural networks; significant wave height; swell; wind waves;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International

Conference_Location

Boston, MA

Print_ISBN

978-1-4244-2807-6

Electronic_ISBN

978-1-4244-2808-3

Type

conf

DOI

10.1109/IGARSS.2008.4779998

Filename

4779998