Salinity retrieval from SMOS brightness temperatures

Author

Labroue, S. ; Obligis, E. ; Boone, C. ; Philipps, S.

Author_Institution

Space Oceanogr. Div., Ramonville, France

Volume

4

fYear

2003

fDate

21-25 July 2003

Firstpage

2771

Abstract

The neural network methodology is applied to the sea surface salinity retrieval from SMOS brightness temperatures. The direct model for simulating the brightness temperatures is the Small Slope Approximation model (SSA). Different cases are compared to analyze the retrieval quality. The effect of a bias on the brightness temperatures and of the instrumental accuracy expected on the SMOS measurements are evaluated. The inversion algorithm is improved when adding ancillary parameters (sea surface temperature, wind speed and a priori salinity).

Keywords

approximation theory; microwave imaging; neural nets; oceanographic techniques; remote sensing; seawater; wind; ancillary parameters; brightness temperatures; inversion algorithm; neural network; sea surface salinity retrieval; sea surface temperature; small slope approximation model; soil moisture-ocean salinity; wind speed; Brightness temperature; Computational modeling; Databases; Neural networks; Neurons; Ocean temperature; Sea measurements; Sea surface; Testing; Wind speed;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International

Print_ISBN

0-7803-7929-2

Type

conf

DOI

10.1109/IGARSS.2003.1294580

Filename

1294580