Articles | Volume 14, issue 3
https://doi.org/10.5194/essd-14-1193-2022
https://doi.org/10.5194/essd-14-1193-2022
Data description paper
 | 
16 Mar 2022
Data description paper |  | 16 Mar 2022

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

Xing Yan, Zhou Zang, Zhanqing Li, Nana Luo, Chen Zuo, Yize Jiang, Dan Li, Yushan Guo, Wenji Zhao, Wenzhong Shi, and Maureen Cribb

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Short summary
This study developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF was extensively evaluated against ground-truth AERONET data and tested on a global scale against conventional satellite-based FMF products to demonstrate its superiority in accuracy.
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