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INCREASING THE SPATIAL COVERAGE OF ATMOSPHERIC AEROSOL DEPTH MEASUREMENTS USING RANDOM FOREST AND MEAN FILTERS.

Zhongying Wang1, Rafael Pires de Lima1, James L Crooks2

  • 1Department of Geography, University of Colorado Boulder.

IEEE International Geoscience and Remote Sensing Symposium Proceedings. International Geoscience and Remote Sensing Symposium
|February 13, 2026
PubMed
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This study introduces a random forest model to improve satellite-derived aerosol optical depth (AOD) coverage. The new method enhances air quality data for better pollution and health impact assessments.

Keywords:
Aerosol Optical DepthImputationRandom Forest

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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Public Health

Background:

  • Aerosols significantly influence atmospheric chemistry, cloud formation, climate, and human health.
  • Satellite-derived aerosol optical depth (AOD) data has limited spatial coverage due to factors like clouds and surface conditions.
  • Incomplete AOD data hinders accurate particulate matter modeling and health studies.

Purpose of the Study:

  • To develop a method for increasing the spatial coverage of AOD data.
  • To create high-resolution, daily AOD maps for the conterminous U.S.
  • To improve air pollution characterization for health and environmental studies.

Main Methods:

  • A random forest model was trained to predict AOD, capturing spatial dependencies.
  • The model was combined with and without mean filters to maximize data imputation.
  • The approach generated full-coverage, high-resolution daily AOD data for the conterminous U.S.

Main Results:

  • Achieved significantly higher spatial coverage for daily AOD data.
  • Produced high-resolution AOD estimates across the conterminous U.S.
  • The enhanced AOD data is suitable for detailed air pollutant concentration studies.

Conclusions:

  • The developed random forest model effectively overcomes limitations of satellite AOD coverage.
  • The full-coverage AOD data provides a valuable resource for atmospheric research and public health assessments.
  • This method advances the utilization of AOD data in understanding air quality and its impacts.