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
Lei Fan
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
Related authors
No articles found.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Wanjun Zhang, Thomas Scholten, Steffen Seitz, Qianmei Zhang, Guowei Chu, Linhua Wang, Xin Xiong, and Juxiu Liu
Hydrol. Earth Syst. Sci., 28, 3837–3854, https://doi.org/10.5194/hess-28-3837-2024, https://doi.org/10.5194/hess-28-3837-2024, 2024
Short summary
Short summary
Rainfall input generally controls soil water and plant growth. We focus on rainfall redistribution in succession sequence forests over 22 years. Some changes in rainwater volume and chemistry in the throughfall and stemflow and drivers were investigated. Results show that shifted open rainfall over time and forest factors induced remarkable variability in throughfall and stemflow, which potentially makes forecasting future changes in water resources in the forest ecosystems more difficult.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-147, https://doi.org/10.5194/essd-2024-147, 2024
Manuscript not accepted for further review
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice mapping method using to address the challenges of cloud contamination and missing data in optical remote sensing observations. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed strong correlation with statistical data.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943, https://doi.org/10.5194/hess-27-1929-2023, https://doi.org/10.5194/hess-27-1929-2023, 2023
Short summary
Short summary
This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
Short summary
Short summary
We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2851–2864, https://doi.org/10.5194/essd-14-2851-2022, https://doi.org/10.5194/essd-14-2851-2022, 2022
Short summary
Short summary
In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 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.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
Short summary
Short summary
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.
Q. Kuang and Y. P. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 103–108, https://doi.org/10.5194/isprs-archives-XLII-3-W9-103-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-103-2019, 2019
Y. C. Zheng, L. L. Li, and Y. P. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 239–243, https://doi.org/10.5194/isprs-archives-XLII-3-W9-239-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-239-2019, 2019
Yilong Wang, Philippe Ciais, Grégoire Broquet, François-Marie Bréon, Tomohiro Oda, Franck Lespinas, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, Haoran Xu, Shu Tao, Kevin R. Gurney, Geoffrey Roest, Diego Santaren, and Yongxian Su
Earth Syst. Sci. Data, 11, 687–703, https://doi.org/10.5194/essd-11-687-2019, https://doi.org/10.5194/essd-11-687-2019, 2019
Short summary
Short summary
We address the question of the global characterization of fossil fuel CO2 emission hotspots that may cause coherent XCO2 plumes in space-borne CO2 images, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. For space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 hotspots are identified, covering 72 % of the global emissions. These hotspots define the targets for the purpose of monitoring fossil fuel CO2 emissions from space.
Wen-Jun Zhou, Hua-Zheng Lu, Yi-Ping Zhang, Li-Qing Sha, Douglas Allen Schaefer, Qing-Hai Song, Yun Deng, and Xiao-Bao Deng
Biogeosciences, 13, 5487–5497, https://doi.org/10.5194/bg-13-5487-2016, https://doi.org/10.5194/bg-13-5487-2016, 2016
Short summary
Short summary
Throughfall and litter leachate DOC fluxes amounted to 6.81 and 7.23 % of the net ecosystem exchange, respectively. The surface soil was a sink of above water transported DOC. The analysis of 13° C hydrological water DOC and soil, leaf and litter indicated that DOC is transformed in the surface soil. Soil respiration is more dependent on water transported DOC flux than soil-water content (at 0–20 cm), and most sensitive to soil-water DOC flux (at 0–20 cm) compared to soil temperature.
Related subject area
Domain: ESSD – Land | Subject: Biogeosciences and biodiversity
VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
Crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of the United States: reconciliation of top-down and bottom-up data sources
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI
A synthesized field survey database of vegetation and active-layer properties for the Alaskan tundra (1972–2020)
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
A spectral-structural characterization of European temperate, hemiboreal and boreal forests
TCSIF: a temporally consistent global Global Ozone Monitoring Experiment-2A (GOME-2A) solar-induced chlorophyll fluorescence dataset with the correction of sensor degradation
National forest carbon harvesting and allocation dataset for the period 2003 to 2018
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022
EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
Reference maps of soil phosphorus for the pan-Amazon region
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Investigating limnological processes and modern sedimentation at Lake Żabińskie, northeast Poland: a decade-long multi-variable dataset, 2012–2021
Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
Global climate-related predictors at kilometer resolution for the past and future
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
Rates and timing of chlorophyll-a increases and related environmental variables in global temperate and cold-temperate lakes
Harmonized gap-filled datasets from 20 urban flux tower sites
Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
LegacyPollen 1.0: a taxonomically harmonized global late Quaternary pollen dataset of 2831 records with standardized chronologies
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Peiyu Cao, Bo Yi, Franco Bilotto, Carlos Gonzalez Fischer, Mario Herrero, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 4557–4572, https://doi.org/10.5194/essd-16-4557-2024, https://doi.org/10.5194/essd-16-4557-2024, 2024
Short summary
Short summary
This article presents a spatially explicit time series dataset reconstructing crop-specific phosphorus fertilizer application rates, timing, and methods at a 4 km × 4 km resolution in the United States from 1850 to 2022. We comprehensively characterized the spatio-temporal dynamics of P fertilizer management over the last 170 years by considering cross-crop variations. This dataset will greatly contribute to the field of agricultural sustainability assessment and Earth system modeling.
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk
Earth Syst. Sci. Data, 16, 4389–4416, https://doi.org/10.5194/essd-16-4389-2024, https://doi.org/10.5194/essd-16-4389-2024, 2024
Short summary
Short summary
Understanding vegetation response to environmental change requires accurate, long-term data on vegetation condition (VC). We evaluated existing satellite VC datasets over Australia and found them lacking, so we developed a new VC dataset for Australia, AusENDVI. It can be used for studying Australia's changing vegetation dynamics and downstream impacts on the carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Kelly S. Aho, Kaelin Cawley, Robert Hensley, Robert O. Hall Jr., Walter Dodds, and Keli Goodman
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-330, https://doi.org/10.5194/essd-2024-330, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
In streams, gas exchange is fundamental to many biogeochemical processes. Gas exchange depends on the degree of saturation and the gas transfer velocity (k). Currently, k is harder to measure than concentration. NEON conducts tracer-gas experiments at 22 streams. Here, we present our processing pipeline to estimate k from these experiments. This dataset (n = 339) represents the largest compilation of standardized k estimates available and captures substantial within- and across-site variability.
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-154, https://doi.org/10.5194/essd-2024-154, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
Short summary
Short summary
To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
Short summary
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
Short summary
Short summary
Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
Short summary
Short summary
Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
Short summary
Short summary
This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
Short summary
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.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
Short summary
Short summary
We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
Short summary
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
Short summary
Short summary
Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
Short summary
Short summary
A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
Short summary
Short summary
Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
Short summary
Short summary
Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
Short summary
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
Short summary
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
Short summary
Short summary
The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
Short summary
Short summary
We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
Short summary
Short summary
This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
Short summary
Short summary
We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
Short summary
Short summary
Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
Short summary
Short summary
The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
Short summary
Short summary
The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
Short summary
Short summary
We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
Short summary
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
Short summary
Short summary
This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
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.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
Short summary
Short summary
Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
Short summary
Short summary
Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
Short summary
Short summary
We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
Short summary
Short summary
Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
Short summary
Short summary
Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
Short summary
Short summary
Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Albert, L. P., Wu, J., Prohaska, N., de Camargo, P. B., Huxman, T. E.,
Tribuzy, E. S., Ivanov, V. Y., Oliveira, R. S., Garcia, S., Smith, M. N.,
Oliveira Junior, R. C., Restrepo-Coupe, N., da Silva, R., Stark, S. C.,
Martins, G. A., Penha, D. V., and Saleska, S. R.: Age-dependent leaf
physiology and consequences for crown-scale carbon uptake during the dry
season in an Amazon evergreen forest, New Phytol., 219, 870–884,
https://doi.org/10.1111/nph.15056, 2018.
Aragao, L. E. O. C, Poulter, B., Barlow, J. B., Anderson, L. O., Malhi, Y.,
Saatchi, S., Phillips, O. L., and Gloor, E.: Environmental change and the
carbon balance of Amazonian forests, Biol. Rev., 89, 913–931,
https://doi.org/10.1111/brv.12088, 2014.
Arora, V. K. and Boer, G. J.: Fire as an interactive component of dynamic
vegetation models, J. Geophys. Res.-Biogeo., 110, G02008,
https://doi.org/10.1029/2005jg000042, 2005.
Barlow, J., Gardner, T. A., Ferreira, L. V., and Peres, C. A.: Litter fall
and decomposition in primary, secondary and plantation forests in the
Brazilian Amazon, Forest Ecol. Manag., 247, 91–97,
https://doi.org/10.1016/j.foreco.2007.04.017, 2007.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais,
N., Rodenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A.,
Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis,
H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C.,
Woodward, F. I., and Papale, D.: Terrestrial gross carbon dioxide uptake:
global distribution and covariation with climate, Science, 329, 834–838,
https://doi.org/10.1126/science.1184984, 2010.
Bernacchi, C. J., Pimentel, C., and Long, S. P.: In vivo temperature
response functions of parameters required to model RuBP-limited
photosynthesis, Plant, Cell Environ., 26, 1419–1430,
https://doi.org/10.1046/j.0016-8025.2003.01050.x, 2003.
Bernacchi, C. J., Bagley, J. E., Serbin, S. P., Ruiz-Vera, U. M., Rosenthal,
D. M., and Vanloocke, A.: Modelling C3 photosynthesis from the
chloroplast to the ecosystem, Plant, Cell Environ., 36, 1641–1657,
https://doi.org/10.1111/pce.12118, 2013.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Brando, P. M., Goetz, S. J., Baccini, A., Nepstad, D. C., Beck, P. S., and
Christman, M. C.: Seasonal and interannual variability of climate and
vegetation indices across the Amazon, P. Natl. Acad. Sci. USA, 107,
14685–14690, https://doi.org/10.1073/pnas.0908741107, 2010.
Chen, X., Maignan, F., Viovy, N., Bastos, A., Goll, D., Wu, J., Liu, L.,
Yue, C., Peng, S., Yuan, W., Conceição, A. C., O'Sullivan, M., and
Ciais, P.: Novel representation of leaf phenology improves simulation of
Amazonian evergreen forest photosynthesis in a land surface model, J. Adv.
Model. Earth Sy., 12, e2018MS001565, https://doi.org/10.1029/2018ms001565, 2020.
Chen, X., Ciais, P., Maignan, F., Zhang, Y., Bastos, A., Liu, L., Bacour,
C., Fan, L., Gentine, P., Goll, D., Green, J., Kim, H., Li, L., Liu, Y.,
Peng, S., Tang, H., Viovy, N., Wigneron, J. P., Wu, J., Yuan, W., and Zhang,
H.: Vapor pressure deficit and sunlight explain seasonality of leaf
phenology and photosynthesis across Amazonian evergreen broadleaved forest,
Global Biogeochem. Cy., 35, e2020GB006893, https://doi.org/10.1029/2020gb006893, 2021.
Chen, X., Huang, Y., Nie, C., Zhang, S., Wang, G., Chen, S., and Chen, Z.: A
long-term reconstructed TROPOMI solar-induced fluorescence dataset using
machine learning algorithms, Sci. Data, 9, 427, https://doi.org/10.1038/s41597-022-01520-1,
2022.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A.,
Brovkin, V., Cox, P. M., Fisher, V., Foley, J. A., Friend, A. D., Kucharik,
C., Lomas, M. R., Ramankutty, N., Sitch, S., Smith, B., White, A., and
Young-Molling, C.: Global response of terrestrial ecosystem structure and
function to CO2 and climate change: results from six dynamic global
vegetation models, Glob. Change Biol., 7, 357–373,
https://doi.org/10.1046/j.1365-2486.2001.00383.x, 2001.
Dantas, M. and Phillipson, J.: Litterfall and litter nutrient content in
primary and secondary Amazonian “terra firme” rain forest, J. Trop. Ecol.,
5, 27–36, https://doi.org/10.1017/s0266467400003199, 1989.
Davidson, E. A., de Araújo, A. C., Artaxo, P., Balch, J. K., Brown, I.
F., Bustamante, M. M., Coe, M. T., DeFries, R. S., Keller, M., Longo, M.,
Munger, J. W., Schroeder, W., Soares-Filho, B. S., Souza, C. M., and Wofsy, S.
C.: The Amazon basin in transition, Nature, 481, 321–328,
https://doi.org/10.1038/nature10717, 2012.
de Moura, Y. M., Galvão, L. S., Hilker, T., Wu, J., Saleska, S., do
Amaral, C. H., Nelson, B. W., Lopes, A. P., Wiedeman, K. K., Prohaska, N.,
de Oliveira, R. C., Machado, C. B., and Aragão, L. E. O. C.: Spectral
analysis of amazon canopy phenology during the dry season using a tower
hyperspectral camera and modis observations, ISPRS J. Photogramm., 131,
52–64,https://doi.org/10.1016/j.isprsjprs.2017.07.006, 2017.
De Weirdt, M., Verbeeck, H., Maignan, F., Peylin, P., Poulter, B., Bonal, D., Ciais, P., and Steppe, K.: Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model, Geosci. Model Dev., 5, 1091–1108, https://doi.org/10.5194/gmd-5-1091-2012, 2012.
Dechant, B., Ryu, Y., Badgley, G., Zeng, Y., Berry, J. A., Zhang, Y.,
Goulas, Y., Li, Z., Zhang, Q., Kang, M., Li, J., and Moya, I.: Canopy
structure explains the relationship between photosynthesis and sun-induced
chlorophyll fluorescence in crops, Remote Sens. Environ., 241,
1–17, https://doi.org/10.1016/j.rse.2020.111733, 2020.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Doughty, C. E. and Goulden, M. L.: Seasonal patterns of tropical forest leaf
area index and CO2 exchange, J. Geophys. Res.-Biogeo., 113, G00B06,
https://doi.org/10.1029/2007jg000590, 2008.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta,
149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Galvão, L. S., dos Santos, J. R., Roberts, D. A., Breunig, F. M.,
Toomey, M., and de Moura, Y. M.: On intra-annual EVI variability in the dry
season of tropical forest: a case study with MODIS and hyperspectral data,
Remote Sens. Environ., 115, 2350–2359, https://doi.org/10.1016/j.rse.2011.04.035, 2011.
Guan, K., Pan, M., Li, H., Wolf, A., Wu, J., Medvigy, D., Caylor, K. K.,
Sheffield, J., Wood, E. F., Malhi, Y., Liang, M., Kimball, J. S., Saleska,
Scott R., Berry, J., Joiner, J., and Lyapustin, A. I.: Photosynthetic
seasonality of global tropical forests constrained by hydroclimate, Nat.
Geosci., 8, 284–289, https://doi.org/10.1038/ngeo2382, 2015.
Guan, K., Berry, J. A., Zhang, Y., Joiner, J., Guanter, L., Badgley, G., and
Lobell, D. B.: Improving the monitoring of crop productivity using spaceborne
solar-induced fluorescence, Glob. Change Biol., 22, 716–726,
https://doi.org/10.1111/gcb.13136, 2016.
Harper, A. B., Cox, P. M., Friedlingstein, P., Wiltshire, A. J., Jones, C. D., Sitch, S., Mercado, L. M., Groenendijk, M., Robertson, E., Kattge, J., Bönisch, G., Atkin, O. K., Bahn, M., Cornelissen, J., Niinemets, Ü., Onipchenko, V., Peñuelas, J., Poorter, L., Reich, P. B., Soudzilovskaia, N. A., and Bodegom, P. V.: Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information, Geosci. Model Dev., 9, 2415–2440, https://doi.org/10.5194/gmd-9-2415-2016, 2016.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L.
G.: Overview of the radiometric and biophysical performance of the MODIS
vegetation indices, Remote Sens. Environ., 83, 195–213,
https://doi.org/10.1016/s0034-4257(02)00096-2, 2002.
Huete, A. R., Didan, K., Shimabukuro, Y. E., Ratana, P., Saleska, S. R.,
Hutyra, L. R., Yang, W., Nemani, R. R., and Myneni, R.: Amazon rainforests
green-up with sunlight in dry season, Geophys. Res. Lett., 33, L06405,
https://doi.org/10.1029/2005GL025583, 2006.
June, T., Evans, J. R., and Farquhar, G. D.: A simple new equation for the
reversible temperature dependence of photosynthetic electron transport: a
study on soybean leaf, Funct. Plant. Biol., 31, 275–283, https://doi.org/10.1071/FP03250,
2004.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74,
https://doi.org/10.1038/s41597-019-0076-8, 2019.
Kartikeyan, B., Sarkar, A., and Majumder, K. L.: A segmentation approach to
classification of remote sensing imagery, Int. J. Remote Sens., 19,
1695–1709, https://doi.org/10.1080/014311698215199, 1998.
Kobayashi, K. and Salam, M. U.: Comparing simulated and measured values
using mean squared deviation and its components, Agron. J., 92, 345–352,
https://doi.org/10.1007/s100870050043, 2000.
Leff, J. W., Wieder, W. R., Taylor, P. G., Townsend, A. R., Nemergut, D. R.,
Grandy, A. S., and Cleveland, C. C.: Experimental litterfall manipulation
drives large and rapid changes in soil carbon cycling in a wet tropical
forest. Glob. Change Biol., 18, 2969–2979, https://doi.org/10.1111/j.1365-2486.2012.02749.x,
2012.
Li, Q., Chen, X., Yuan, W., Lu, H., Shen, R., Wu, S., Gong, F., Dai, Y.,
Liu, L., Sun, Q., Zhang, C., and Su, Y.: Remote sensing of seasonal climatic
constraints on leaf phenology across pantropical evergreen forest biome,
Earth's Future, 9, e2021EF002160, https://doi.org/10.1029/2021EF002160, 2021.
Li, X. and Xiao, J.: Mapping photosynthesis solely from solar-induced
chlorophyll fluorescence: A global, fine-resolution dataset of gross primary
production derived from OCO-2, Remote Sens., 11, 2563, https://doi.org/10.3390/rs11212563,
2019.
Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig,
S., Eamus, D., de Dios, Victor R., Mitchell, P., Ellsworth, D. S., de Beeck,
M. O., Wallin, G., Uddling, J., Tarvainen, L., Linderson, M.-L., Cernusak,
L. A., Nippert, J. B., Ocheltree, T. W., Tissue, D. T., Martin-StPaul, N.
K., Rogers, A., Warren, J. M., De Angelis, P., Hikosaka, K., Han, Q., Onoda,
Y., Gimeno, T. E., Barton, C. V. M., Bennie, J., Bonal, D., Bosc, A.,
Löw, M., Macinins-Ng, C., Rey, A., Rowland, L., Setterfield, S. A.,
Tausz-Posch, S., Zaragoza-Castells, J., Broadmeadow, M. S. J., Drake, J. E.,
Freeman, M., Ghannoum, O., Hutley, Lindsay B., Kelly, J. W., Kikuzawa, K.,
Kolari, P., Koyama, K., Limousin, J.-M., Meir, P., Lola da Costa, A. C.,
Mikkelsen, T. N., Salinas, N., Sun, W., and Wingate, L.: Optimal stomatal
behaviour around the world, Nat. Clim. Change, 5, 459–464,
https://doi.org/10.1038/nclimate2550, 2015.
Lopes, A. P., Nelson, B. W., Wu, J., Graça, P. M. L. D. A., Tavares, J.
V., Prohaska, N., Martins, G. A., and Saleska, S. R.: Leaf flush drives dry
season green-up of the Central Amazon, Remote Sens. Environ., 182, 90–98,
https://doi.org/10.1016/j.rse.2016.05.009, 2016.
Maes, W. H., Gentine, P., Verhoest, N. E. C., and Miralles, D. G.: Potential evaporation at eddy-covariance sites across the globe, Hydrol. Earth Syst. Sci., 23, 925–948, https://doi.org/10.5194/hess-23-925-2019, 2019.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate,
L.: Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Glob. Change Biol. 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Melgosa, M., Huertas, R., and Berns, R. S.: Performance of recent advanced
color-difference formulas using the standardized residual sum of squares
index, J. Opt. Soc. Am. A, 25, 1828–1834, https://doi.org/10.1364/JOSAA.25.001828, 2008.
Menezes, J., Garcia, S., Grandis, A., Nascimento, H., Domingues, T. F.,
Guedes, A. V., Aleixo, I., Camargo, P., Campos, J., Damasceno, A.,
Dias-Silva, R., Fleischer, K., Kruijt, B., Cordeiro, A. L., Martins, N. P.,
Meir, P., Norby, R. J., Pereira, I., Portela, B., Rammig, A., Ribeiro, A.
G., Lapola, D. M., and Quesada, C. A.: Changes in leaf functional traits
with leaf age: when do leaves decrease their photosynthetic capacity in
Amazonian trees?, Tree. Physiol., 42, 922–938, https://doi.org/10.1093/treephys/tpab042,
2021.
Merkl, R. and Waack, S.: Bioinformatik interaktiv, John Wiley & Sons,
ISBN 978-3-527-32594-8, 2009.
Midoko Iponga, D., Mpikou, R. G. J., Loumeto, J., and Picard, N.: The effect
of different anthropogenic disturbances on litterfall of a dominant pioneer
rain forest tree in Gabon, Afr. J. Ecol., 58, 281–290, https://doi.org/10.1111/aje.12696,
2019.
Myneni, R. B., Yang, W., Nemani, R. R., Huete, A. R., Dickinson, R. E.,
Knyazikhin, Y., Didan, K., Fu, R., Negrón Juárez, R. I., Saatchi, S.
S., Hashimoto, H., Ichii, K., Shabanov, N. V., Tan, B., Ratana, P.,
Privette, J. L., Morisette, J. T., Vermote, E. F., Roy, D. P., Wolfe, R. E.,
Friedl, M. A., Running, S. W., Votava, P., El-Saleous, N., Devadiga, S., Su,
Y., and Salomonson, V. V.: Large seasonal swings in leaf area of Amazon
rainforests, P. Natl. Acad. Sci. USA, 104, 4820–4823,
https://doi.org/10.1073/pnas.0611338104, 2007.
Ndakara, O. E.: Litterfall and nutrient returns in isolated stands of persea
gratissima (Avocado Pear) in the rainforest zone of southern nigeria,
Ethiopian Journal of Environmental Studies and Management, 4, 42–50,
https://doi.org/10.4314/ejesm.v4i3.6, 2011.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A.,
Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P.,
Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A.,
Sitch, S., and Hayes, D.: A large and persistent carbon sink in the world's
forests, Science, 333, 988–993, https://doi.org/10.1126/science.1201609, 2011.
Pearson, K.: VII. Mathematical contributions to the theory of evolution.
III. Regression, heredity, and panmixia, Philos. T.
Roy. Soc. A, 187, 253–318, https://doi.org/10.1098/rsta.1896.0007, 1896.
Piao, S., Fang, J., Zhou, L., Ciais, P., and Zhu, B.: Variations in
satellite-derived phenology in China's temperate vegetation, Glob. Change
Biol., 12, 672–685, https://doi.org/10.1111/j.1365-2486.2006.01123.x, 2006.
Restrepo-Coupe, N., Levine, N. M., Christoffersen, B. O., Albert, L. P., Wu,
J., Costa, M. H., Galbraith, D., Imbuzeiro, H., Martins, G., da Araujo, A.
C., Malhi, Y. S., Zeng, X., Moorcroft, P., and Saleska, S. R.: Do dynamic
global vegetation models capture the seasonality of carbon fluxes in the
Amazon basin? A data-model intercomparison, Glob. Change Biol., 23, 191–208,
https://doi.org/10.1111/gcb.13442, 2017.
Ryu, Y., Baldocchi, D. D., Kobayashi, H., van Ingen, C., Li, J., Black, T.
A., Beringer, J., van Gorsel, E., Knohl, A., Law, B. E., and Roupsard, O.:
Integration of MODIS land and atmosphere products with a coupled-process
model to estimate gross primary productivity and evapotranspiration from 1
km to global scales, Global Biogeochem. Cy., 25, GB4017,
https://doi.org/10.1029/2011gb004053, 2011.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land
products of shortwave radiation and diffuse and total photosynthetically
active radiation at 5 km resolution from 2000, Remote Sens. Environ., 204,
812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108,
9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C.,
da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H.
C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J. W., Pyle,
E. H., Rice, A. H., and Silva, H.: Carbon in Amazon forests: unexpected
seasonal fluxes and disturbance-induced losses, Science, 302, 1554–1557,
https://doi.org/10.1126/science.1091165, 2003.
Saleska, S. R., Didan, K., Huete, A. R., and da Rocha, H. R.: Amazon forests
green-up during 2005 drought, Science, 318, 612, https://doi.org/10.1126/science.1146663,
2007.
Sayer, E. J., Heard, M. S., Grant, H. K., Marthews, T. R., and Tanner, E. V.
J.: Soil carbon release enhanced by increased tropical forest litterfall,
Nat. Clim. Change, 1, 304–307, https://doi.org/10.1038/nclimate1190, 2011.
Smith, M. N., Stark, S. C., Taylor, T. C., Ferreira, M. L., de Oliveira, E.,
Restrepo-Coupe, N., Chen, S., Woodcock, T., dos Santos, D. B., Alves, L. F.,
Figueira, M., de Camargo, P. B., de Oliveira, R. C., Aragão, L. E. O.
C., Falk, D. A., McMahon, S. M., Huxman, T. E., and Saleska, S. R.: Seasonal
and drought-related changes in leaf area profiles depend on height and light
environment in an Amazon forest, New Phytol., 222, 1284–1297,
https://doi.org/10.1111/nph.15726, 2019.
Sulla-Menashe, D., Woodcock, C. E., and Friedl, M. A.: Canadian boreal
forest greening and browning trends: An analysis of biogeographic patterns
and the relative roles of disturbance versus climate drivers, Environ. Res.
Lett., 13, 014007, https://doi.org/10.1088/1748-9326/aa9b88, 2018.
Tang, H. and Dubayah, R.: Light-driven growth in Amazon evergreen forests
explained by seasonal variations of vertical canopy structure, P. Natl. Acad. Sci. USA, 114, 2640–2644, https://doi.org/10.1073/pnas.1616943114, 2017.
Toomey, M., Roberts, D. A., and Nelson, B.: The influence of epiphylls on
remote sensing of humid forests, Remote Sens. Environ., 113, 1787–1798,
https://doi.org/10.1016/j.rse.2009.04.002, 2009.
Wang, C., Li, J., Liu, Q., Zhong, B., Wu, S., and Xia, C.: Analysis of
differences in phenology extracted from the enhanced vegetation index and
the leaf area index, Sensors, 17, 1982, https://doi.org/10.3390/s17091982, 2017.
Weiss, A. and Norman, J. M.: Partitioning solar radiation into direct and
diffuse, visible and near-infrared components, Agr. Forest Meteorol., 34,
205–213, https://doi.org/10.1016/0168-1923(85)90020-6, 1985.
Wu, J., Albert, L. P., Lopes, A. P., Restrepo-Coupe, N., Hayek, M.,
Wiedemann, K. T., Guan, K., Stark, S. C., Christoffersen, B., Prohaska, N.,
Tavares, J. V., Marostica, S., Kobayashi, H., Ferreira, M. L., Campos, K.
S., da Silva, R., Brando, P. M., Dye, D. G., Huxman, T. E., Huete, A. R.,
Nelson, B. W., and Saleska, S. R.: Leaf development and demography explain
photosynthetic seasonality in Amazon evergreen forests, Science, 351,
972–976, https://doi.org/10.1126/science.aad5068, 2016.
Wu, J., Serbin, S. P., Xu, X., Albert, L. P., Chen, M., Meng, R., Saleska,
S. R., and Rogers, A.: The phenology of leaf quality and its within-canopy
variation is essential for accurate modeling of photosynthesis in tropical
evergreen forests, Glob. Change Biol., 23, 4814–4827, https://doi.org/10.1111/gcb.13725,
2017.
Wu, J., Kobayashi, H., Stark, S. C., Meng, R., Guan, K., Tran, N. N., Gao,
S., Yang, W., Restrepo-Coupe, N., Miura, T., Oliviera, R. C., Rogers, A.,
Dye, D. G., Nelson, B. W., Serbin, S. P., Huete, A. R., and Saleska, S. R.:
Biological processes dominate seasonality of remotely sensed canopy
greenness in an Amazon evergreen forest, New Phytol., 217, 1507–1520,
https://doi.org/10.1111/nph.14939, 2018.
Xiao, X., Zhang, Q., Saleska, S., Hutyra, L., De Camargo, P., Wofsy, S.,
Frolking, S., Boles, S., Keller, M., and Moore, B.: Satellite-based modeling
of gross primary production in a seasonally moist tropical evergreen forest,
Remote Sens. Environ., 94, 105–122, https://doi.org/10.1016/j.rse.2004.08.015, 2005.
Xu, L., Saatchi, S. S., Yang, Y., Myneni, R. B., Frankenberg, C., Chowdhury,
D., and Bi, J.: Satellite observation of tropical forest seasonality:
spatial patterns of carbon exchange in Amazonia, Environ. Res. Lett., 10,
084005, https://doi.org/10.1088/1748-9326/10/8/084005, 2015.
Xu, X., Medvigy, D., Joseph Wright, S., Kitajima, K., Wu, J., Albert, L. P.,
Martins, G. A., Saleska, S. R., and Pacala, S. W.: Variations of leaf
longevity in tropical moist forests predicted by a trait-driven carbon
optimality model, Ecol. Lett., 20, 1097–1106, https://doi.org/10.1111/ele.12804, 2017.
Yang, X., Tang, J., Mustard, J. F., Lee, J.-E., Rossini, M., Joiner, J.,
Munger, J. W., Kornfeld, A., and Richardson, A. D.: Solar-induced
chlorophyll fluorescence that correlates with canopy photosynthesis on
diurnal and seasonal scales in a temperate deciduous forest, Geophys. Res.
Lett., 42, 2977–2987, https://doi.org/10.1002/2015gl063201, 2015.
Yang, X., Wu, J., Chen, X., Ciais, P., Maignan, F., Yuan, W., Piao, S.,
Yang, S., Gong, F., Su, Y., Dai, Y., Liu, L., Zhang, H., Bonal, D., Liu, H.,
Chen, G., Lu, H., Wu, S., Fan, L., Gentine, P., and Wright, S. J.: A
comprehensive framework for seasonal controls of leaf abscission and
productivity in evergreen broadleaved tropical and subtropical forests,
Innovation, 2, 100154, https://doi.org/10.1016/j.xinn.2021.100154, 2021.
Yang, X., Chen, X., Ren, J., Yuan, W., Liu, L., Liu, J., Chen, D., Xiao, Y.,
Song, Q., Du, Y., Wu, S., Fan, L., Dai, X., Wang, Y., and Su, Y.: Leaf
age-dependent LAI seasonality product (Lad-LAI) over tropical and
subtropical evergreen broadleaved forests, Figshare [data set],
https://doi.org/10.6084/m9.figshare.21700955.v4, 2022.
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., Ryu,
Y., Chen, G., Dong, W., Hu, Z., Jain, A. K., Jiang, C., Kato, E., Li, S.,
Lienert, S., Liu, S., Nabel, J., Qin, Z., Quine, T., Sitch, S., Smith, W.
K., Wang, F., Wu, C., Xiao, Z., and Yang, S.: Increased atmospheric vapor
pressure deficit reduces global vegetation growth, Sci. Adv., 5, eaax1396,
https://doi.org/10.1126/sciadv.aax1396, 2019.
Zhao, P., Gao, L., Wei, J., Ma, M., Deng, H., Gao, J., and Chen, X.:
Evaluation of ERA-Interim air temperature data over the Qilian Mountains of
China, Adv. Meteorol., 2020, 7353482, https://doi.org/10.1155/2020/7353482, 2020.
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...
Altmetrics
Final-revised paper
Preprint