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

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

doi:https://doi.org/10.5194/essd-14-1193-2022

Articles | Volume 14, issue 3

https://doi.org/10.5194/essd-14-1193-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-1193-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 3

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

Supplement

https://doi.org/10.5194/essd-14-1193-2022-supplement

Data sets

Physical and deep learning retrieved fine mode fraction (Phy-DL FMF) Xing Yan https://doi.org/10.5281/zenodo.5105617

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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.