A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

<p>The aerosol fine-mode fraction (FMF) is valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable over land. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizin...

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Bibliographic Details
Main Authors: X. Yan, Z. Zang, Z. Li, N. Luo, C. Zuo, Y. Jiang, D. Li, Y. Guo, W. Zhao, W. Shi, M. Cribb
Format: Article
Language:English
Published: Copernicus Publications 2022-03-01
Series:Earth System Science Data
Online Access:https://essd.copernicus.org/articles/14/1193/2022/essd-14-1193-2022.pdf

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https://essd.copernicus.org/articles/14/1193/2022/essd-14-1193-2022.pdf

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