High-resolution post-process corrected satellite AOD

Abstract

Poor air quality poses a great threat to human health. Accurate high-resolution satellite remote sensing of atmospheric aerosols would highly benefit satellite-based air quality estimates. We have developed and validated a post-process correction and downscaling approach for satellite remote sensing of aerosols. We use NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over Washington D.C. -Baltimore area during the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign in 2011 to evaluate our approach. We derive and evaluate the AOD fields with high 250 meter resolution. The results show that the post-process correction approach is suitable for deriving downscaled, high-resolution AOD estimates and significantly improves the accuracy of the AOD retrievals.

Post-process correction improves the accuracy of satellite PM2.5 retrievals

“…The AOD-to-PM 2.5 ratio is estimated by machine learning techniques utilizing a fusion of collocated ground stationbased in-situ PM 2.5 data, MERRA-2 reanalysis model AOD and PM 2.5 data, spectral AERONET AOD, satellite-observed spectral top-of-atmosphere reflectances, meteorology data and various high-resolution geographical indicators representing, for example, population density and land surface elevation. Utilizing these data, we employ the post-process correction approach to the estimation of AOD-to-PM 2.5 ratio (Lipponen et al, 2021(Lipponen et al, , 2022Taskinen et al, 2022) and then the high-resolution PM 2.5 retrieval is obtained as the product of the post-process corrected AOD-to-PM 2.5 ratio and POPCORN AOD. By using an ensemble of neural networks, we can also provide error envelopes for the machine learning related uncertainty in the PM 2.5 estimates.…”