Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5491-2023
© 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-15-5491-2023
© 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
| 
06 Dec 2023
Data description paper |
| 06 Dec 2023
| 06 Dec 2023
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
Kristof Van Tricht
CORRESPONDING AUTHOR
VITO, Mol, 2400, Belgium
Jeroen Degerickx
VITO, Mol, 2400, Belgium
Sven Gilliams
VITO, Mol, 2400, Belgium
Daniele Zanaga
VITO, Mol, 2400, Belgium
Marjorie Battude
CS Group France, Toulouse, 31506, France
Alex Grosu
CS Group Romania, Craiova, 200692, Romania
Joost Brombacher
eLEAF B.V., Wageningen, 6703CT, the Netherlands
Myroslava Lesiv
International Institute for Applied Systems Analysis (IIASA), Laxenburg, 2361, Austria
Juan Carlos Laso Bayas
International Institute for Applied Systems Analysis (IIASA), Laxenburg, 2361, Austria
Santosh Karanam
International Institute for Applied Systems Analysis (IIASA), Laxenburg, 2361, Austria
Steffen Fritz
International Institute for Applied Systems Analysis (IIASA), Laxenburg, 2361, Austria
Inbal Becker-Reshef
Department of Geographical Sciences, University of Maryland, College Park, USA
Belén Franch
Global Change Unit, Image Processing Laboratory, Universitat de Valencia, Paterna (Valencia), Spain
Bertran Mollà-Bononad
Global Change Unit, Image Processing Laboratory, Universitat de Valencia, Paterna (Valencia), Spain
Hendrik Boogaard
Wageningen Environmental Research (WENR), Wageningen University & Research, Wageningen, 6708 PB, the Netherlands
Arun Kumar Pratihast
Wageningen Environmental Research (WENR), Wageningen University & Research, Wageningen, 6708 PB, the Netherlands
Benjamin Koetz
European Space Agency, Paris, France
Zoltan Szantoi
European Space Agency, Paris, France
Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch 7602, South Africa
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Cited
14 citations as recorded by crossref.
- Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals A. Descals et al. 10.3390/rs17020340
- A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019 S. Fritz et al. 10.1088/1748-9326/ad6a71
- Principles for satellite monitoring of vegetation carbon uptake I. Prentice et al. 10.1038/s43017-024-00601-6
- Comparative evaluation of the accuracy of mapping irrigated areas using sentinel 1 images in the Bilate and Gumara watersheds, Ethiopia A. Yimer et al. 10.1080/23311916.2024.2357728
- A Systematic Review of the Use of Deep Learning in Satellite Imagery for Agriculture B. Victor et al. 10.1109/JSTARS.2024.3501216
- Improved generality of wheat green LAI models through mitigation of the effect of leaf chlorophyll content variation with red edge vegetation indices W. Li et al. 10.1016/j.rse.2024.114589
- High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset G. Bessardon et al. 10.3390/land13111811
- Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation J. Baumert et al. 10.1016/j.ecoinf.2024.102836
- Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning L. Parente et al. 10.1038/s41597-024-04139-6
- The development of an operational system for estimating irrigation water use reveals socio-political dynamics in Ukraine J. Dari et al. 10.5194/hess-28-2651-2024
- Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping I. Aneece et al. 10.14358/PERS.24-00026R2
- High-resolution mapping of global winter-triticeae crops using a sample-free identification method Y. Fu et al. 10.5194/essd-17-95-2025
- The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. 10.1002/qj.4858
- Dynamic global-scale crop and irrigation monitoring L. See et al. 10.1038/s43016-023-00841-7
13 citations as recorded by crossref.
- Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals A. Descals et al. 10.3390/rs17020340
- A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019 S. Fritz et al. 10.1088/1748-9326/ad6a71
- Principles for satellite monitoring of vegetation carbon uptake I. Prentice et al. 10.1038/s43017-024-00601-6
- Comparative evaluation of the accuracy of mapping irrigated areas using sentinel 1 images in the Bilate and Gumara watersheds, Ethiopia A. Yimer et al. 10.1080/23311916.2024.2357728
- A Systematic Review of the Use of Deep Learning in Satellite Imagery for Agriculture B. Victor et al. 10.1109/JSTARS.2024.3501216
- Improved generality of wheat green LAI models through mitigation of the effect of leaf chlorophyll content variation with red edge vegetation indices W. Li et al. 10.1016/j.rse.2024.114589
- High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset G. Bessardon et al. 10.3390/land13111811
- Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation J. Baumert et al. 10.1016/j.ecoinf.2024.102836
- Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning L. Parente et al. 10.1038/s41597-024-04139-6
- The development of an operational system for estimating irrigation water use reveals socio-political dynamics in Ukraine J. Dari et al. 10.5194/hess-28-2651-2024
- Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping I. Aneece et al. 10.14358/PERS.24-00026R2
- High-resolution mapping of global winter-triticeae crops using a sample-free identification method Y. Fu et al. 10.5194/essd-17-95-2025
- The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. 10.1002/qj.4858
1 citations as recorded by crossref.
Latest update: 30 Jan 2025
Short summary
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.
WorldCereal is a global mapping system that addresses food security challenges. It provides...
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