Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4997-2023
© 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-15-4997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
15 Nov 2023
Data description article |
| 15 Nov 2023
| 15 Nov 2023
A new cropland area database by country circa 2020
Francesco N. Tubiello
CORRESPONDING AUTHOR
Statistics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
Giulia Conchedda
Statistics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
Leon Casse
Statistics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
Pengyu Hao
Digitalization and Informatics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
Giorgia De Santis
Statistics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
Zhongxin Chen
Digitalization and Informatics Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy
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Total article views: 7,778 (including HTML, PDF, and XML)
Cumulative views and downloads
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| HTML | XML | Total | BibTeX | EndNote | |
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| 5,460 | 2,151 | 167 | 7,778 | 174 | 215 |
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- XML: 167
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Total article views: 5,914 (including HTML, PDF, and XML)
Cumulative views and downloads
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| HTML | XML | Total | BibTeX | EndNote | |
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| 4,452 | 1,324 | 138 | 5,914 | 150 | 188 |
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- PDF: 1,324
- XML: 138
- Total: 5,914
- BibTeX: 150
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Total article views: 1,864 (including HTML, PDF, and XML)
Cumulative views and downloads
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| HTML | XML | Total | BibTeX | EndNote | |
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Viewed (geographical distribution)
Total article views: 7,778 (including HTML, PDF, and XML)
Thereof 7,560 with geography defined
and 218 with unknown origin.
Total article views: 5,914 (including HTML, PDF, and XML)
Thereof 5,736 with geography defined
and 178 with unknown origin.
Total article views: 1,864 (including HTML, PDF, and XML)
Thereof 1,824 with geography defined
and 40 with unknown origin.
| Country | # | Views | % |
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23 citations as recorded by crossref.
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- An Updated Inventory of Fluorspar CaF<sub>2</sub> Production, Industrial Use, and Emissions of Trifluoroacetic Acid (TFA) from 1930, Including the Period from 2000 to 2020 A. Lindley https://doi.org/10.4236/gep.2025.1311008
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- The potential of irrigation for cereals production in Sub–Saharan Africa: A machine learning application for emulating crop growth at large scale A. Klinnert et al. https://doi.org/10.1016/j.agwat.2025.109488
- Adaptive Fusion of Multiple Land-Cover Products for Improved Spatial Representation of Key Land Classes in Central Asia L. Fu et al. https://doi.org/10.3390/rs18172894
- Optimizing satellite-based cropland area estimation through integrated map accuracy assessment and stratified sampling design across six African countries A. Adebayo et al. https://doi.org/10.1016/j.jag.2026.105223
- Continental maize mapping and distribution in Africa by integrating radar and optical imagery N. Abdelrahim & S. Jin https://doi.org/10.1007/s10661-025-14502-8
- Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China F. Deng et al. https://doi.org/10.3390/rs16234498
- Widely cited global irrigation statistics lack empirical support A. Puy et al. https://doi.org/10.1093/pnasnexus/pgaf323
- Landscape context outweighs field-scale features in structuring bird communities in West African smallholder rice agroecosystems P. Lopes et al. https://doi.org/10.5194/we-26-103-2026
- Uncertainty of water footprint estimates caused by harvested area inputs B. Demeke et al. https://doi.org/10.1088/3033-4942/ae441e
- Uncertainties in carbon emissions from land use and land cover change in Indonesia I. Brasika et al. https://doi.org/10.5194/bg-22-3547-2025
- Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al. https://doi.org/10.1080/20964471.2025.2578056
- Catch-all classes and invisible crops: A systematic multi-crop comparison of MapBiomas agricultural classification against official agricultural statistics in Brazil B. Escalhão https://doi.org/10.1016/j.geomat.2026.100109
- Pesticides application rate maps in the European Union at a 250 m spatial resolution G. Porta et al. https://doi.org/10.1038/s41597-025-05031-7
- Indirect Land use Change Mechanisms: Tests from Biofuel Mandates and Pantropical Agriculture V. Guye https://doi.org/10.1007/s10640-025-01020-x
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. https://doi.org/10.1038/s41597-024-03247-7
- Climate-driven global cropland changes and consequent feedbacks N. You et al. https://doi.org/10.1038/s41561-025-01724-1
- Quarterly cropland mapping in sample shortage regions using Sentinel-2 images based on the knowledge-driven OCSVM-RF R. Wang et al. https://doi.org/10.1016/j.atech.2025.101572
- Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 2018–2021 from Multi-source Satellite Data Y. Chen et al. https://doi.org/10.34133/remotesensing.1045
- Accuracy Comparison and Synergistic Strategies of Seven High-Resolution Cropland Maps (1–10 m) in China X. Peng et al. https://doi.org/10.3390/rs17173121
23 citations as recorded by crossref.
- A 2020 forest age map for China with 30 m resolution K. Cheng et al. https://doi.org/10.5194/essd-16-803-2024
- An annual cropland extent dataset for Africa at 30 m spatial resolution from 2000 to 2022 Z. Lou et al. https://doi.org/10.5194/essd-17-3777-2025
- An Updated Inventory of Fluorspar CaF<sub>2</sub> Production, Industrial Use, and Emissions of Trifluoroacetic Acid (TFA) from 1930, Including the Period from 2000 to 2020 A. Lindley https://doi.org/10.4236/gep.2025.1311008
- A multi-class, multi-temporal crop and Land cover mapping framework for Morocco using Sentinel-1/2 monthly composites and advanced machine learning ensembles M. Choukri et al. https://doi.org/10.3389/frsen.2026.1827393
- The potential of irrigation for cereals production in Sub–Saharan Africa: A machine learning application for emulating crop growth at large scale A. Klinnert et al. https://doi.org/10.1016/j.agwat.2025.109488
- Adaptive Fusion of Multiple Land-Cover Products for Improved Spatial Representation of Key Land Classes in Central Asia L. Fu et al. https://doi.org/10.3390/rs18172894
- Optimizing satellite-based cropland area estimation through integrated map accuracy assessment and stratified sampling design across six African countries A. Adebayo et al. https://doi.org/10.1016/j.jag.2026.105223
- Continental maize mapping and distribution in Africa by integrating radar and optical imagery N. Abdelrahim & S. Jin https://doi.org/10.1007/s10661-025-14502-8
- Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China F. Deng et al. https://doi.org/10.3390/rs16234498
- Widely cited global irrigation statistics lack empirical support A. Puy et al. https://doi.org/10.1093/pnasnexus/pgaf323
- Landscape context outweighs field-scale features in structuring bird communities in West African smallholder rice agroecosystems P. Lopes et al. https://doi.org/10.5194/we-26-103-2026
- Uncertainty of water footprint estimates caused by harvested area inputs B. Demeke et al. https://doi.org/10.1088/3033-4942/ae441e
- Uncertainties in carbon emissions from land use and land cover change in Indonesia I. Brasika et al. https://doi.org/10.5194/bg-22-3547-2025
- Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
- Mapping annual 30-m paddy rice yield for different cropping systems in mainland Southeast Asia from 2001 to 2021 S. Huan et al. https://doi.org/10.1080/20964471.2025.2578056
- Catch-all classes and invisible crops: A systematic multi-crop comparison of MapBiomas agricultural classification against official agricultural statistics in Brazil B. Escalhão https://doi.org/10.1016/j.geomat.2026.100109
- Pesticides application rate maps in the European Union at a 250 m spatial resolution G. Porta et al. https://doi.org/10.1038/s41597-025-05031-7
- Indirect Land use Change Mechanisms: Tests from Biofuel Mandates and Pantropical Agriculture V. Guye https://doi.org/10.1007/s10640-025-01020-x
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. https://doi.org/10.1038/s41597-024-03247-7
- Climate-driven global cropland changes and consequent feedbacks N. You et al. https://doi.org/10.1038/s41561-025-01724-1
- Quarterly cropland mapping in sample shortage regions using Sentinel-2 images based on the knowledge-driven OCSVM-RF R. Wang et al. https://doi.org/10.1016/j.atech.2025.101572
- Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 2018–2021 from Multi-source Satellite Data Y. Chen et al. https://doi.org/10.34133/remotesensing.1045
- Accuracy Comparison and Synergistic Strategies of Seven High-Resolution Cropland Maps (1–10 m) in China X. Peng et al. https://doi.org/10.3390/rs17173121
Saved (final revised paper)
Latest update: 04 Sep 2026
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
We describe a new dataset of cropland area circa the year 2020, with global coverage and country...
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