NASA Logo

NTRS

NTRS - NASA Technical Reports Server

The auto‑search feature has been disabled based on user feedback. Enter a search term/phrase and click “Search” to begin.

Back to Results
Dust Machine Learning Probability and Assessment - NASA SPoRT introduced the "Dust RGB" via NASA satellites to demonstrate GOES-R ABI capabilities and then evaluated the impact in operations (Fuell et al. 2016)

- The Dust RGB allows for continued dust detection at night, but the cooling ground surface limits the effectiveness as night progresses.

- SPoRT has developed a 'Machine Learning' (ML) model using a physically-based approach which can correctly label 85% of dust pixels and 99% of no-dust pixels
Document ID
20220009552
Acquisition Source
Marshall Space Flight Center
Document Type
Presentation
Authors
Emily Berndt
(Marshall Space Flight Center Redstone Arsenal, Alabama, United States)
Kevin Fuell
(University of Alabama in Huntsville Huntsville, Alabama, United States)
Date Acquired
June 20, 2022
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: National Weather Service SOO Workshop
Location: Albuquerque, NM
Country: US
Start Date: June 23, 2022
Sponsors: National Weather Service
Funding Number(s)
WBS: 740001
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Single Expert
Keywords
Dust
Machine Learning
No Preview Available