  In this paper we present a combined strategy for the retrieval of atmospheric
profiles from infrared sounders. The approach considers the spatial information
and a noise-dependent dimensionality reduction approach. The extracted features
are fed into a canonical linear regression. We compare Principal Component
Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction,
and study the compactness and information content of the extracted features.
Assessment of the results is done on a big dataset covering many spatial and
temporal situations. PCA is widely used for these purposes but our analysis
shows that one can gain significant improvements of the error rates when using
MNF instead. In our analysis we also investigate the relationship between error
rate improvements when including more spectral and spatial components in the
regression model, aiming to uncover the trade-off between model complexity and
error rates.
