Spatial noise-aware temperature retrieval from infrared sounder data

David Malmgren-Hansen, Valero Laparra, Allan Aasbjerg Nielsen, Gustau Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium
Number of pages4
PublisherIEEE
Publication date2017
Pages17-20
DOIs
Publication statusPublished - 2017
Event2017 IEEE International Geoscience and Remote Sensing Symposium - Fort Worth, United States
Duration: 23 Jul 201628 Jun 2017

Conference

Conference2017 IEEE International Geoscience and Remote Sensing Symposium
CountryUnited States
CityFort Worth
Period23/07/201628/06/2017
SeriesIEEE International Geoscience and Remote Sensing Symposium Proceedings
ISSN2153-6996

Keywords

  • Principal component analysis
  • Atmospheric modeling
  • Temperature measurement
  • Eigenvalues and eigenfunctions
  • Temperature distribution
  • Feature extraction
  • Linear regression
  • Infrared Atmospheric Sounding Interferometer (IASI)
  • Minimum Noise Fractions
  • Principal Component Analysis (PCA)
  • Statistical retrieval

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