MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing-Application to LEO and GEO Sensors

Details

• Journal Title:
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Personal Author:
  Maddy, Eric S. ; Boukabara, Sid A.
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; OPPA (Office of Projects, Planning, & Analysis)
• Description:
  In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors.
• Keywords:
  Artificial Intelligence (AI) Atmosphere Earth Observing System Machine Learning Neural Networks Remote Sensing Satellite
• Source:
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8566-8576
• DOI:
  https://doi.org/10.1109/jstars.2021.3104389
• Document Type:
  Journal Article
• Funding:
  Grant no. ST133017CQ0058/1332KP20FNEED0029
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:834c27d1a29d8e6f98250a12bfcf4e4a041528ea7dd7fde00b1c54684f833a1b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/41854/noaa_41854_DS1.pdf
• File Type:
  [PDF - 6.04 MB ]