DocumentCode :
1893105
Title :
Kernel-based retrieval of atmospheric profiles from IASI data
Author :
Camps-Valls, Gustavo ; Laparra, Valero ; Muñoz-Marí, Jordi ; Gómez-Chova, Luis ; Calbet, Xavier
Author_Institution :
Image Process. Lab. (IPL), Univ. de Valencia, Valencia, Spain
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
2813
Lastpage :
2816
Abstract :
This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification of anomalies by supervised classification of discrepancies with the ECMWF estimates. For the retrieval, we observed that KRR clearly outperformed linear regression. Looking at the confidence maps, we observed that big discrepancies are mainly due to the presence of clouds and low emissivities in desert areas. For the identification of anomalies, we observed that the confidence intervals provided by the KRR may help in discarding big errors. High detection accuracy (around 90%) is achieved by a support vector machine, which largely outperforms standard linear and nonlinear classifiers.
Keywords :
atmospheric humidity; atmospheric techniques; atmospheric temperature; clouds; ECMWF estimates; Infrared Atmospheric Sounding Interferometer; MetOp-IASI data; atmospheric profiles; atmospheric property; desert areas; humidity atmospheric profile; hyperspectral infrared sounding spectra; kernel ridge regression; linear regression; standard nonlinear classifier; support vector machine; surface property; temperature atmospheric profile; Accuracy; Atmospheric modeling; Clouds; Kernel; Meteorology; Support vector machines; Training; IASI; Kernel methods; atmospheric retrieval; kernel ridge regression; support vector machine (SVM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6049799
Filename :
6049799
Link To Document :
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