Spatiotemporal Gap‐Filling of NASA Deep Blue Satellite Aerosol Optical Depth Over the Contiguous United States (CONUS) Using the UNet 3+ Architecture

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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Bibliographic Details
Main Authors: Jeffrey S. M. Lee, S. Marcela Loría‐Salazar, Heather A. Holmes, Andrew M. Sayer
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2025-07-01
Series:Earth and Space Science
Subjects:
air‐quality
deep‐learning
aerosols and particles
machine learning
Online Access:https://doi.org/10.1029/2025EA004338
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https://doi.org/10.1029/2025EA004338

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