Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder
Lu Liang,
Jacob Daniels,
Michael Biancardi
et al.
Abstract:Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spe… Show more
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