Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder
Description:
Article describes how Aerosol Optical Depth (AOD)is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. 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 Spectroradiometer (MODIS) multi-angle implementation of atmospheric correction (MAIAC) products.
Date:
November 30, 2023
Creator:
Liang, Lu; Daniels, Jacob; Biancardi, Micahel & Zhou, Yuye
Item Type:
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Partner:
UNT College of Engineering