Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5333-2022
© 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-14-5333-2022
© 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
| 
07 Dec 2022
Data description paper |
| 07 Dec 2022
| 07 Dec 2022
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
Changhao Xiong
School of Remote Sensing and Information Engineering, Wuhan University, Hubei 430010, China
Qian Wang
Faculty of Geography, Beijing Normal University, Beijing 100875, China
Aolin Jia
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Bing Li
Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China
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Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential...
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