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Atmospheric Correction Inter-comparison eXercise.

Georgia Doxani1, Eric Vermote2, Jean-Clause Roger2,3

  • 1SERCO SpA c/o European Space Agency ESA-ESRIN, Largo Galileo Galilei, 00044 Frascati, Italy.

Remote Sensing
|July 25, 2020
PubMed
Summary
This summary is machine-generated.

The Atmospheric Correction Inter-comparison eXercise (ACIX) evaluated surface reflectance products from multiple atmospheric correction processors. Results varied by sensor and site, highlighting processor strengths and weaknesses for future algorithm improvements.

Keywords:
Landsat-8Sentinel-2aerosol optical thicknessatmospheric correctionprocessors inter-comparisonremote sensingsurface reflectancewater vapour

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Area of Science:

  • Earth Observation
  • Remote Sensing
  • Atmospheric Science

Background:

  • State-of-the-art atmospheric correction (AC) processors generate Surface Reflectance (SR) products crucial for Earth observation.
  • Aerosol Optical Thickness (AOT) and Water Vapour (WV) are key atmospheric parameters influencing SR accuracy.

Purpose of the Study:

  • To establish an international framework for inter-comparing various AC processors.
  • To analyze the performance of different AC algorithms using Landsat-8 and Sentinel-2 data.
  • To identify the strengths and limitations of current AC approaches.

Main Methods:

  • The Atmospheric Correction Inter-comparison eXercise (ACIX) was established as an open initiative for AC processor developers.
  • Twelve participants applied their AC algorithms to Landsat-8 and Sentinel-2 image datasets from diverse global sites.
  • An inter-comparison protocol was developed to evaluate SR products, AOT, and WV outputs.

Main Results:

  • Inter-comparison results showed divergence based on sensor, product type, and geographical site.
  • The study identified specific strengths and weaknesses of the participating AC processors.
  • This initial ACIX experiment provided valuable insights into the performance of different AC algorithms.

Conclusions:

  • The ACIX framework effectively highlighted the performance variations among AC processors.
  • The exercise served as a learning opportunity for developers to understand their algorithms' advantages and limitations.
  • Future ACIX experiments are planned to investigate algorithm improvements and enhanced performances.