Articles | Volume 12, issue 4 
            
                
                    
            
            
            https://doi.org/10.5194/essd-12-3545-2020
                    © Author(s) 2020. 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-12-3545-2020
                    © Author(s) 2020. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps
Qiangyi Yu
                                            Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
                                        
                                    Liangzhi You
                                            Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
                                        
                                    
                                            International Food Policy Research Institute (IFPRI), Washington DC,
USA
                                        
                                    Ulrike Wood-Sichra
                                            International Food Policy Research Institute (IFPRI), Washington DC,
USA
                                        
                                    Yating Ru
                                            International Food Policy Research Institute (IFPRI), Washington DC,
USA
                                        
                                    Alison K. B. Joglekar
                                            GEMS Agroinformatics Initiative, University of Minnesota, Saint Paul,
Minnesota, USA
                                        
                                    Steffen Fritz
                                            International Institute for Applied Systems Analysis (IIASA),
Laxenburg, Austria
                                        
                                    Wei Xiong
                                            International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
                                        
                                    Miao Lu
                                            Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
                                        
                                    Wenbin Wu
CORRESPONDING AUTHOR
                                            
                                    
                                            Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
                                        
                                    
                                            Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
                                        
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Zhenrong Du, Le Yu, Yue Zhao, Xinyue Li, Xiaoxuan Liu, Xiyu Li, Pengyu Hao, Zhongxin Chen, Zhe Guo, Liangzhi You, Xiaorui Ma, and Hongyu Wang
                                    Earth Syst. Sci. Data, 17, 5543–5556, https://doi.org/10.5194/essd-17-5543-2025, https://doi.org/10.5194/essd-17-5543-2025, 2025
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                                                We created the first global maps showing where livestocks have been raised each year from 1961 to 2021. These maps help to see how livestock numbers and locations have changed over time. Using global statistics and satellite data, we built a model to estimate livestock density at a high resolution (5 km). This work supports better decisions in food security, disease control, and environmental protection around the world.
                                            
                                            
                                        Myroslava Lesiv, Steffen Fritz, Martina Duerauer, Ivelina Georgieva, Marcel Buchhorn, Luc Bertels, Nandika Tsendbazar, Ruben Van De Kerchove, Daniele Zanaga, Dmitry Schepaschenko, Linda See, Martin Herold, Bruno Smets, Michael Cherlet, and Ian Mccallum
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-468, https://doi.org/10.5194/essd-2025-468, 2025
                                    Revised manuscript accepted for ESSD 
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                                                This paper presents a unique global reference data set for land cover mapping at a 10 m resolution, aligned with Sentinel-2 imagery for the year 2015. It contains more than 16.5 million data records at a 10 m resolution (or 165 K data records at 100 m) and information on 12 different land cover classes. The data set was collected by a group of experts through visual interpretation of very high resolution imagery, along with other sources of information provided in the Geo-Wiki platform.
                                            
                                            
                                        Clément Bourgoin, Astrid Verhegghen, Silvia Carboni, Iban Ameztoy, Lucas Degreve, Steffen Fritz, Martin Herold, Nandika Tsendbazar, Myroslava Lesiv, Fréderic Achard, and René Colditz
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-351, https://doi.org/10.5194/essd-2025-351, 2025
                                    Preprint under review for ESSD 
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                                                In the context of the EU Deforestation Regulation (EUDR), forest maps can support operators in the assessment of the risk of deforestation after year 2020. Here we present the Global Forest Cover map of year 2020, derived from the combination of most recent publicly available land cover and land use datasets. The map is a globally-consistent representation of the presence/absence of forests based on EUDR definitions, but its use is not mandatory, not exclusive and not legally binding.
                                            
                                            
                                        Zihui Zhang, Lang Xia, Fen Zhao, Yue Gu, Jing Yang, Yan Zha, Shangrong Wu, and Peng Yang
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-516, https://doi.org/10.5194/essd-2024-516, 2025
                                    Revised manuscript under review for ESSD 
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                                                We utilized multi-source data and a deep learning model to explore the annual mapping of rice for Northeast China from 1985 to 2023. First, a rice training dataset comprising 155 images was created. Then, we developed the annual result enhancement (ARE) method to diminish the impact of the limited training sample. In comparison to traditional rice mapping methods, the accuracy of results obtained using the ARE method is significantly improved.
                                            
                                            
                                        Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
                                    Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
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                                                WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
                                            
                                            
                                        Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
                                    Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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                                                Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
                                            
                                            
                                        Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
                                    Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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                                                Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
                                            
                                            
                                        Michele Ferri, Uta Wehn, Linda See, Martina Monego, and Steffen Fritz
                                    Hydrol. Earth Syst. Sci., 24, 5781–5798, https://doi.org/10.5194/hess-24-5781-2020, https://doi.org/10.5194/hess-24-5781-2020, 2020
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                                                As part of the flood risk management strategy of the 
Brenta-Bacchiglione catchment (Italy), a citizen observatory for flood risk management is currently being implemented. A cost–benefit analysis of the citizen observatory was undertaken to demonstrate the value of this approach in monetary terms. Results show a reduction in avoided damage of 45 % compared to a scenario without implementation of the citizen observatory. The idea is to promote this methodology for future flood risk management.
                                            
                                            
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https://doi.org/10.1016/j.scitotenv.2016.10.223, 2017. 
                    
                Short summary
                    SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
                    SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the...
                    
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