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22 October 2010 Nonlinear retrieval of atmospheric profiles from MetOp-IASI and MTG-IRS data
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This paper evaluates the potential use of nonlinear retrieval methods to derive cloud, surface and atmospheric properties from hyperspectral MetOp-IASI and MTG-IRS spectra. The methods are compared in terms of both accuracy and speed with the current IASI and IRS L2 PPFP implementation, which consists of a principal component extraction, typically referred as to Empirical Orthogonal Functions (EOF), and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering 1) other linear feature extraction methods instead of EOF, such as (orthonormalized) partial least squares, and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks (NN) and kernel ridge regression (KRR) as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models outperform the linear retrieval both in the presence of noise and noise-free settings, and for both IASI and IRS synthetic and real data. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. These results confirm the validity of the proposed approach for retrieval of atmospheric profiles.
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Gustavo Camps-Valls, Luis Guanter, Jordi Muñoz-Marí, Luis Gómez-Chova, and Xavier Calbet "Nonlinear retrieval of atmospheric profiles from MetOp-IASI and MTG-IRS data", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300Z (22 October 2010);

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