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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Cited
16 citations as recorded by crossref.
- The first gap-free 20 m 5-day LAI/FAPAR products over China (2018–2023) from integrated Landsat-8/9 and Sentinel-2 Analysis Ready Data H. Ma et al. 10.1016/j.rse.2025.115048
- A dataset of forest regrowth in globally key deforestation regions J. Zang et al. 10.1038/s41597-025-04481-3
- A global dataset of forest regrowth following wildfires J. Zang et al. 10.1038/s41597-024-03896-8
- Dynamic parameterization of global land surface albedo components: Bare soil, non-photosynthetic vegetation, and photosynthetic vegetation A. Jia et al. 10.1016/j.rse.2025.114943
- An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance G. Zhang et al. 10.1109/JSTARS.2024.3428481
- Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation X. Zhang et al. 10.1109/JSTARS.2024.3522938
- Global daily 1 km gapless XCO₂ (2003−2023) derived from multi-satellite observations and a spatiotemporal deep learning framework J. Wang 10.1016/j.eiar.2025.108146
- A Comprehensive Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products of MODIS, GLASS, and GEO Over China L. Xia et al. 10.1109/JSTARS.2025.3575839
- Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data L. Zhang et al. 10.1016/j.envint.2023.108057
- Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2 P. Naethe et al. 10.1016/j.rse.2024.114013
- Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution J. Hackländer et al. 10.7717/peerj.16972
- Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes F. Wang et al. 10.1038/s41477-025-02096-5
- Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products X. Liang et al. 10.5194/essd-16-177-2024
- An overview of the high-resolution global LAnd surface satellite (Hi-GLASS) products suite S. Liang et al. 10.1016/j.srs.2025.100263
- Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning J. Li et al. 10.1016/j.fcr.2023.109102
- Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021 H. Ma et al. 10.5194/essd-14-5333-2022
14 citations as recorded by crossref.
- The first gap-free 20 m 5-day LAI/FAPAR products over China (2018–2023) from integrated Landsat-8/9 and Sentinel-2 Analysis Ready Data H. Ma et al. 10.1016/j.rse.2025.115048
- A dataset of forest regrowth in globally key deforestation regions J. Zang et al. 10.1038/s41597-025-04481-3
- A global dataset of forest regrowth following wildfires J. Zang et al. 10.1038/s41597-024-03896-8
- Dynamic parameterization of global land surface albedo components: Bare soil, non-photosynthetic vegetation, and photosynthetic vegetation A. Jia et al. 10.1016/j.rse.2025.114943
- An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance G. Zhang et al. 10.1109/JSTARS.2024.3428481
- Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation X. Zhang et al. 10.1109/JSTARS.2024.3522938
- Global daily 1 km gapless XCO₂ (2003−2023) derived from multi-satellite observations and a spatiotemporal deep learning framework J. Wang 10.1016/j.eiar.2025.108146
- A Comprehensive Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products of MODIS, GLASS, and GEO Over China L. Xia et al. 10.1109/JSTARS.2025.3575839
- Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data L. Zhang et al. 10.1016/j.envint.2023.108057
- Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2 P. Naethe et al. 10.1016/j.rse.2024.114013
- Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution J. Hackländer et al. 10.7717/peerj.16972
- Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes F. Wang et al. 10.1038/s41477-025-02096-5
- Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products X. Liang et al. 10.5194/essd-16-177-2024
- An overview of the high-resolution global LAnd surface satellite (Hi-GLASS) products suite S. Liang et al. 10.1016/j.srs.2025.100263
2 citations as recorded by crossref.
- Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning J. Li et al. 10.1016/j.fcr.2023.109102
- Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021 H. Ma et al. 10.5194/essd-14-5333-2022
Latest update: 03 Nov 2025
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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