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.
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.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Mar 2022
Data description paper |
| 16 Mar 2022
| 16 Mar 2022
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Zhou Zang
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Department of Atmospheric and Oceanic Science and ESSIC, University of
Maryland, College Park, MD, 20740, USA
Nana Luo
School of Geomatics and Urban Spatial Informatics, Beijing University
of Civil Engineering and Architecture, Beijing 102612, China
Chen Zuo
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Yize Jiang
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Dan Li
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Yushan Guo
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Wenji Zhao
College of Resource Environment and Tourism, Capital Normal
University, Beijing, China
Wenzhong Shi
Department of Land Surveying and Geo-Informatics, The Hong Kong
Polytechnic University, Hong Kong, China
Maureen Cribb
Department of Atmospheric and Oceanic Science and ESSIC, University of
Maryland, College Park, MD, 20740, USA
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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.
This study developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by...
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