Spatiotemporal Gap‐Filling of NASA Deep Blue Satellite Aerosol Optical Depth Over the Contiguous United States (CONUS) Using the UNet 3+ Architecture
Abstract Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete and have gaps caused by clouds and bright surfaces. These gaps represent a barrier in characterizing daily aerosol loadings, which is important for air quality applications....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2025-07-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025EA004338 |
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| Summary: | Abstract Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete and have gaps caused by clouds and bright surfaces. These gaps represent a barrier in characterizing daily aerosol loadings, which is important for air quality applications. In particular, recent studies in aerosol studies have shown satellite AOD to be a useful predictor of particulate matter, but are often limited to monthly or longer temporal resolution because of missing AOD retrievals. In this study, we propose using a UNet 3+ to fill gaps in satellite AOD retrievals. We tested the hypothesis that UNet 3+ trained on deep blue (DB) AOD and supplemental data sets (e.g., Modern‐Era Retrospective analysis for Research and Applications, Version 2 reanalysis AOD, meteorological and land‐use variables from North American Mesoscale Forecast System, and Hazard Mapping System smoke polygons) will improve the availability of AOD data accurately. We created spatiotemporal data sets of daily, gap‐filled DB AOD from 2012 to 2023 over the CONtinental United States (CONUS) at a 12 × 12 km2 resolution. We were able to train the model and perform the gap‐filling in ∼10 hr, resulting in an increase of AOD data availability by 281%. We demonstrated that our approach is feasible over CONUS through quantitative and qualitative evaluations against AERONET and DB AOD. In statistical evaluations, our gap‐filled AOD data set attained an RMSE ∼ 0.09 and a r ∼ 0.87 against collocated AERONET retrievals, compared to an RMSE ∼ 0.11 and a r ∼ 0.86 that the original DB AOD retrievals scored against AERONET. We plan to use this data set for future air quality and health investigations. |
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| ISSN: | 2333-5084 |