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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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
Preprint under review 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.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
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This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Tanja Cegnar, Hendrik Boogaard, Klara Finkele, Branislava Lalic, Joanna Raymond, Saskia Lifka, David M. Schultz, and Vieri Tarchiani
Adv. Sci. Res., 20, 9–16, https://doi.org/10.5194/asr-20-9-2023, https://doi.org/10.5194/asr-20-9-2023, 2023
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Agrometeorological services often do not cover the
last mile– not reaching, not being understood, nor being trusted by smallholder farmers living in remote areas. To help bridge this gap across the last mile, the workshop on effective communication of agrometeorological services took place during the EMS2022. This paper presents the outcomes and recommendations on how to bridge the gap between information providers and information users.
G. Mosomtai, J. L. Kasiiti, R. M. Murithi, P. Sandström, T. Landmann, O. W. Lwande, O. A. Hassan, C. Ahlm, R. Sang, M. Evander, Z. Szantoi, and G. Ottavianelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 211–216, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-211-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-211-2023, 2023
M. Hosseini, I. Becker-Reshef, R. Sahajpal, P. Lafluf, G. Leale, E. Puricelli, S. Skakun, and H. McNairn
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 405–410, https://doi.org/10.5194/isprs-annals-V-3-2022-405-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-405-2022, 2022
Zoltan Szantoi, Andreas Brink, and Andrea Lupi
Earth Syst. Sci. Data, 13, 3767–3789, https://doi.org/10.5194/essd-13-3767-2021, https://doi.org/10.5194/essd-13-3767-2021, 2021
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The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km2 of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.
Adrià Descals, Serge Wich, Erik Meijaard, David L. A. Gaveau, Stephen Peedell, and Zoltan Szantoi
Earth Syst. Sci. Data, 13, 1211–1231, https://doi.org/10.5194/essd-13-1211-2021, https://doi.org/10.5194/essd-13-1211-2021, 2021
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Decision-making for sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil-producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global oil palm area was 21±0.42 Mha for 2019; however, young and sparse plantations were not included in this estimate.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
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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.
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.
Zoltan Szantoi, Andreas Brink, Andrea Lupi, Claudio Mammone, and Gabriel Jaffrain
Earth Syst. Sci. Data, 12, 3001–3019, https://doi.org/10.5194/essd-12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020, 2020
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Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km2 from which 153 665 km2 is mapped with eight land cover classes while 406 776 km2 is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.
Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue
Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, https://doi.org/10.5194/essd-12-1913-2020, 2020
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Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
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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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