Classification and retrieval of dry snow parameters by means of SMM/I data and artificial neural networks

Author

Tedesco, M. ; Pampaloni, P. ; Pulliainen, J. ; Hallikainen, M.

Author_Institution

IFAC-CNR, Firenze, Italy

Volume

1

fYear

2003

fDate

21-25 July 2003

Firstpage

330

Abstract

Dry snow temperature, snow water equivalent (SWE) and snow depth have been retrieved by using the 19 and 37 GHz SSM/I brightness temperatures and artificial neural networks (ANNs). The results obtained have been compared with those obtained using other approaches such as the spectral polarization difference, the HUT model-based iterative inversion, the Chang algorithm and linear regressions. In general, it has been noted that the ANN based technique gives better results than the other approaches, which tend to underestimate the unknown parameters.

Keywords

data acquisition; hydrological techniques; image classification; neural nets; radiometry; regression analysis; remote sensing; snow; 19 GHz; 37 GHz; ANN; Chang algorithm; HUT model-based iterative inversion; SMM/I data; SSM/I brightness temperatures; SWE; artificial neural networks; dry snow parameter classification; dry snow parameter retrieval; dry snow temperature; linear regressions; passive microwave remote sensing; snow depth; snow water equivalent; spectral polarization difference; Artificial neural networks; Brightness temperature; Information retrieval; Iterative algorithms; Iterative methods; Linear regression; Neural networks; Passive microwave remote sensing; Polarization; Snow;

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.2003.1293766

Filename

1293766