Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2601-2023
© Author(s) 2023. 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-15-2601-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jun 2023
Data description paper |
| 27 Jun 2023
| 27 Jun 2023
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Xueqin Yang
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University and Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519082, China
Key Lab of Guangdong for Utilization of Remote Sensing and
Geographical Information System, Guangdong Open Laboratory of Geospatial
Information Technology and Application, Guangzhou Institute of Geography,
Guangdong Academy of Sciences, Guangzhou 510070, China
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences,
Guangzhou 510640, China
Xiuzhi Chen
CORRESPONDING AUTHOR
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University and Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519082, China
Jiashun Ren
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University and Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519082, China
College of Earth Sciences, Chengdu University of Technology, Chengdu 610000, China
Wenping Yuan
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University and Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519082, China
Liyang Liu
Laboratoire des Sciences du Climat et de l'Environnement, IPSL,
CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Juxiu Liu
Dinghushan Forest Ecosystem Research Station, South China Botanical
Garden, Chinese Academy of Sciences, Guangzhou 510650, China
Dexiang Chen
Pearl River Delta Forest Ecosystem Research Station, Research
Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510650, China
Yihua Xiao
Pearl River Delta Forest Ecosystem Research Station, Research
Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510650, China
Qinghai Song
CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China
Yanjun Du
Key Laboratory of Genetics and Germplasm Innovation of Tropical
Special Forest Trees and Ornamental Plants (Ministry of Education), College
of Forestry, Hainan University, Haikou 570228, China
Shengbiao Wu
School of Biological Sciences, University of Hong Kong, Pokfulam, Hong Kong SAR, China
Chongqing Jinfo Mountain Karst Ecosystem National Observation and
Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
Xiaoai Dai
College of Earth Sciences, Chengdu University of Technology, Chengdu 610000, China
Yunpeng Wang
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences,
Guangzhou 510640, China
Yongxian Su
Key Lab of Guangdong for Utilization of Remote Sensing and
Geographical Information System, Guangdong Open Laboratory of Geospatial
Information Technology and Application, Guangzhou Institute of Geography,
Guangdong Academy of Sciences, Guangzhou 510070, China
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In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
Short summary
Short summary
For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
Short summary
Short summary
We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
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Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index...
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