Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-15-2024
© Author(s) 2024. 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-16-15-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Jan 2024
Data description paper |
| 04 Jan 2024
| 04 Jan 2024
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Jiabin Pu
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Samapriya Roy
Arizona Data Science Initiative, University of Arizona, Tucson, AZ 85721, USA
Zaichun Zhu
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Miina Rautiainen
School of Engineering, Aalto University, P.O. Box 14100, 00076 Aalto, Finland
Yuri Knyazikhin
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Ranga B. Myneni
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
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Short summary
Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation...
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