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Saharan dust detection using multi-sensor satellite measurements.

Sriharsha Madhavan1, John J Qu2, X Hao2

  • 1Environmental Science and Technology Center, Department of Geography and Geoinformation Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; NOAA National Environmental Satellite, Data, and Information Service Center for Satellite Applications and Research, E/RA3, 5830 University Research Ct., College Park, MD 20740, USA; Science Systems and Applications Inc., 10210 Greenbelt Rd., Lanham, MD 20706, USA.

Heliyon
|February 17, 2017
PubMed
Summary
This summary is machine-generated.

Scientists improved Saharan dust detection using multi-sensor data. This enhances understanding of North Atlantic climate and hurricane formation by improving aerosol optical thickness measurements.

Keywords:
Atmospheric scienceEnvironmental scienceGeographyGeologyGeophysics

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Area of Science:

  • Atmospheric Science
  • Climate Science
  • Remote Sensing

Background:

  • The North Atlantic Basin is crucial for hurricane genesis.
  • Saharan dust significantly impacts West African and Atlantic basin climate.
  • Accurate dust detection is vital for climate modeling.

Purpose of the Study:

  • To enhance Saharan dust detection schemes.
  • To improve the accuracy of aerosol optical thickness (AOT) measurements.
  • To better understand the climatology of the North Atlantic.

Main Methods:

  • Utilized multi-sensor measurements from Terra/Aqua MODIS and OMI.
  • Employed additional thermal infrared channels (8.55 μm, 7.1 μm, 13.1 μm).
  • Fused data for improved dust pixel identification and segregation from other aerosols.

Main Results:

  • Achieved a higher correlation coefficient (R²=0.85) between dust index and AOT.
  • Improved MODIS dust measurement radiometry by an order of 2.
  • Enhanced spatial measurements by a factor of at least 10.

Conclusions:

  • The enhanced dust detection algorithm significantly improves AOT product accuracy.
  • Better dust detection aids in understanding climate impacts and hurricane formation.
  • Multi-sensor data fusion offers a robust approach for aerosol characterization.