Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression

Joaquin Garcia-Sobrino, Joan Serra-Sagrista, Valero Laparra, Xavier Calbet, Gustau Camps-Valls

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


© 1980-2012 IEEE. The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors.
Original languageEnglish
Article number7819481
Pages (from-to)2213-2224
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
Publication statusPublished - 1 Apr 2017


  • Infrared atmospheric sounding interferometer (IASI)
  • JPEG 2000
  • kernel methods
  • lossy compression
  • near-lossless compression
  • spectral transforms
  • statistical retrieval


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