Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 14, issue 3
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

Viewed

Total article views: 4,220 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,026 1,071 123 4,220 367 112 151
  • HTML: 3,026
  • PDF: 1,071
  • XML: 123
  • Total: 4,220
  • Supplement: 367
  • BibTeX: 112
  • EndNote: 151
Views and downloads (calculated since 30 Sep 2021)
Cumulative views and downloads (calculated since 30 Sep 2021)

Viewed (geographical distribution)

Total article views: 4,220 (including HTML, PDF, and XML) Thereof 3,995 with geography defined and 225 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Nov 2025
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Read more
Share
Altmetrics
Final-revised paper
Preprint