  Aerosols play a critical role in atmospheric chemistry, and affect clouds,
climate, and human health. However, the spatial coverage of satellite-derived
aerosol optical depth (AOD) products is limited by cloud cover, orbit patterns,
polar night, snow, and bright surfaces, which negatively impacts the coverage
and accuracy of particulate matter modeling and health studies relying on air
pollution characterization. We present a random forest model trained to capture
spatial dependence of AOD and produce higher coverage through imputation. By
combining the models with and without the mean filters, we are able to create
full-coverage high-resolution daily AOD in the conterminous U.S., which can be
used for aerosol estimation and other studies leveraging air pollutant
concentration levels.
