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Bi-Spectral Infrared Algorithm for Cloud Coverage over Oceans by the JEM-EUSO Mission Program.

David Santalices1, Susana Briz1, Antonio J de Castro1

  • 1Laboraty of Infrared, Universidad Carlos III de Madrid, Av. de la Universidad, 30, 28911 Madrid, Spain.

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Summary
This summary is machine-generated.

A new method uses infrared camera data to create cloud maps for space missions. This technique accurately identifies cloud cover, aiding in the study of cosmic rays.

Keywords:
JEM-EUSOcloud coverageinfrared cameraremote sensingsplit-window algorithm

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Area of Science:

  • Earth and Space Science
  • Atmospheric Science
  • Remote Sensing

Background:

  • Monitoring Earth's atmosphere requires high spatial and temporal resolution, driving demand for low-cost, low-power, lightweight systems.
  • The Joint Experiment Missions-Extreme Universe Space Observatory (JEM-EUSO) program aims to study Ultra-High Energy Cosmic Rays (UHECRs) using atmospheric fluorescence detection.
  • UV instruments in space platforms require accurate cloudiness information within their field of view, necessitating the inclusion of infrared cameras.

Purpose of the Study:

  • To evaluate a novel method for generating binary cloudiness masks (CM) over oceans using bi-spectral infrared (IR) camera data.
  • To develop and validate an algorithm for cloud detection suitable for space-based platforms like those in the JEM-EUSO program.
  • To introduce novel statistical procedures for estimating brightness temperature and determining classification thresholds.

Main Methods:

  • Utilizing Moderate-Resolution Imaging Spectroradiometer (MODIS) bands 31 and 32 to create a database for training and validation.
  • Implementing a split-window algorithm that compares measured brightness temperature with statistically estimated values.
  • Defining classification thresholds based on scenario-dependent statistical analysis of IR image databases and ancillary sea surface temperature data.

Main Results:

  • The developed algorithm successfully generates binary cloudiness masks (CM) with over 90% agreement compared to MODIS CM.
  • The algorithm's performance is comparable to existing cloud detection methods in remote sensing.
  • Discrepancies are primarily concentrated at cloud edges, indicating a need for further refinement in these areas.

Conclusions:

  • The proposed bi-spectral IR camera method offers a accurate and efficient way to generate cloud maps for space-based atmospheric monitoring.
  • The novel statistical approach for brightness temperature estimation and threshold determination contributes to improved cloud detection accuracy.
  • The high accuracy achieved makes this method a valuable asset for the JEM-EUSO program and similar space missions requiring real-time cloud information.