Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 101408, China
Qingling Sun
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Jie Tian
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Wenping Yuan
College of Urban and Environmental Sciences, School of Urban Planning and Design, Peking University, Beijing 100871, China
Liyang Liu
Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Wei Zheng
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Mei Wang
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Yunpeng Wang
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 101408, China
Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
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2,449
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3,428
349
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162
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BibTeX: 96
EndNote: 162
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Cumulative views and downloads
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Total article views: 2,601 (including HTML, PDF, and XML)
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1,853
633
115
2,601
181
71
99
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PDF: 633
XML: 115
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Supplement: 181
BibTeX: 71
EndNote: 99
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596
201
30
827
168
25
63
HTML: 596
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Supplement: 168
BibTeX: 25
EndNote: 63
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Total article views: 3,428 (including HTML, PDF, and XML)
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Total article views: 2,601 (including HTML, PDF, and XML)
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Understanding how leaves absorb carbon from the atmosphere is essential for predicting changes in global forests. Young leaves play a key role in this process, but their efficiency has been difficult to measure at large scales. Using satellite data, we developed a new method to track the seasonal patterns of young leaves’ photosynthetic capacity from 2001 to 2018. Our dataset helps scientists better understand forest growth and how ecosystems respond to climate change.
Understanding how leaves absorb carbon from the atmosphere is essential for predicting changes...