Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-3777-2025
© Author(s) 2025. 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-17-3777-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An annual cropland extent dataset for Africa at 30 m spatial resolution from 2000 to 2022
Zihang Lou
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Dailiang Peng
CORRESPONDING AUTHOR
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Zhou Shi
CORRESPONDING AUTHOR
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Hongyan Wang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P. R. China, Beijing 100048, China
Ke Liu
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P. R. China, Beijing 100048, China
Yaqiong Zhang
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
Xue Yan
Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
Botany Department, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000, Nairobi, 00200, Kenya
Zhongxing Chen
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Su Ye
Zhejiang Key Laboratory of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Department of Earth System Science, Tsinghua University, Beijing 100084, China
Jinkang Hu
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100094, China
Yulong Lv
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100094, China
Hao Peng
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China
University of Chinese Academy of Sciences, Beijing 100094, China
Yizhou Zhang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100094, China
Bing Zhang
CORRESPONDING AUTHOR
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
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Cited articles
Adolph, B., Jellason, N. P., Kwenye, J. M., Davies, J., Dray, A. G., Waeber, P. O., Jeary, K., and Franks, P.: Exploring farmers' decisions on agricultural intensification and cropland expansion in Ethiopia, Ghana, and Zambia through serious gaming, Land, 12, 556, https://doi.org/10.3390/land12030556, 2023.
Ahlqvist, O.: In Search of Classification that Supports the Dynamics of Science: The FAO Land Cover Classification System and Proposed Modifications, Environ. Plann. B, 35, 169–186, https://doi.org/10.1068/b3344, 2008.
Belgiu, M.: Random forest in remote sensing: a review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., and Tait, A. M.: Dynamic World, Near real-time global 10 m land use land cover mapping, Sci. Data, 9, 251, https://doi.org/10.1038/s41597-022-01307-4, 2022.
Burton, C., Yuan, F., Ee-Faye, C., Halabisky, M., Ongo, D., Mar, F., Addabor, V., Mamane, B., and Adimou, S.: Co-production of a 10 m cropland extent map for continental africa using sentinel-2, cloud computing, and the open-data-cube, Authorea, https://doi.org/10.1002/essoar.10510081.1, 2022.
Cassidy, E. S., West, P. C., Gerber, J. S., and Foley, J. A.: Redefining agricultural yields: from tonnes to people nourished per hectare, Environ. Res. Lett., 8, 34015, https://doi.org/10.1088/1748-9326/8/3/034015, 2013.
Chamberlin, J., Jayne, T. S., and Headey, D.: Scarcity amidst abundance? Reassessing the potential for cropland expansion in Africa, Food Policy, 48, 51–65, https://doi.org/10.1016/j.foodpol.2014.05.002, 2014.
Chen, B., Wu, S., Jin, Y., Song, Y., Wu, C., Venevsky, S., Xu, B., Webster, C., and Gong, P.: Wildfire risk for global wildland–urban interface areas, Nat. Sustain., 7, 474–484, https://doi.org/10.1038/s41893-024-01291-0, 2024.
Chen, S., Olofsson, P., Saphangthong, T., and Woodcock, C. E.: Monitoring shifting cultivation in Laos with landsat time series, Remote Sens. Environ., 288, 113507, https://doi.org/10.1016/j.rse.2023.113507, 2023.
Crawford, C. L., Wiebe, R. A., Yin, H., Radeloff, V. C., and Wilcove, D. S.: Biodiversity consequences of cropland abandonment, Nat. Sustain., 7, 1596–1607, https://doi.org/10.1038/s41893-024-01452-1, 2024.
Cui, Y., Dong, J., Zhang, C., Yang, J., Chen, N., Guo, P., Di, Y., Chen, M., Li, A., and Liu, R.: Validation and refinement of cropland map in southwestern china by harnessing ten contemporary datasets, Sci. Data, 11, 671, https://doi.org/10.1038/s41597-024-03508-5, 2024.
Dara, A., Baumann, M., Kuemmerle, T., Pflugmacher, D., Rabe, A., Griffiths, P., Hölzel, N., Kamp, J., Freitag, M., and Hostert, P.: Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual landsat time series, Remote Sens. Environ., 213, 49–60, https://doi.org/10.1016/j.rse.2018.05.005, 2018.
Debonne, N., Van Vliet, J., Ramkat, R., Snelder, D., and Verburg, P.: Farm scale as a driver of agricultural development in the kenyan rift valley, Agr. Syst., 186, 102943, https://doi.org/10.1016/j.agsy.2020.102943, 2021.
Decuyper, M., Chávez, R. O., Lohbeck, M., Lastra, J. A., Tsendbazar, N., Hackländer, J., Herold, M., and Vågen, T.-G.: Continuous monitoring of forest change dynamics with satellite time series, Remote Sens. Environ., 269, 112829–112841, https://doi.org/10.1016/j.rse.2021.112829, 2022.
Duan, J., Ren, C., Wang, S., Zhang, X., Reis, S., Xu, J., and Gu, B.: Consolidation of agricultural land can contribute to agricultural sustainability in China, Nat. Food, 2, 1014–1022, https://doi.org/10.1038/s43016-021-00415-5, 2021.
ESA: Land Cover CCI Product User Guide Version 2. Tech. Rep., http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 31 July 2025), 2017.
Fader, M., Gerten, D., Krause, M., Lucht, W., and Cramer, W.: Spatial decoupling of agricultural production and consumption: quantifying dependences of countries on food imports due to domestic land and water constraints, Environ. Res. Lett., 8, 14046, https://doi.org/10.1088/1748-9326/8/1/014046, 2013.
FAO: The State of Agricultural Commodity Markets 2020: Agricultural markets and sustainable development – Global value chains, smallholder farmers and digital innovations. Rome, Italy, https://doi.org/10.4060/cb0665en, 2020.
FAO, IFAD, UNICEF, WFP, and WHO: The State of Food Security and Nutrition in the World 2024 – Financing to end hunger, food insecurity and malnutrition in all its forms, FAO, IFAD, UNICEF, WFP, WHO, Rome, Italy, https://doi.org/10.4060/cd1254en, 2024.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2025.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A., Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C., Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G., Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F., Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner, S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov, A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M., Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and field size, Glob. Change Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838, 2015.
Giller, K. E., Delaune, T., Silva, J. V., van Wijk, M., Hammond, J., Descheemaeker, K., van de Ven, G., Schut, A. G. T., Taulya, G., Chikowo, R., and Andersson, J. A.: Small farms and development in sub-saharan africa: farming for food, for income or for lack of better options?, Food Secur., 13, 1431–1454, https://doi.org/10.1007/s12571-021-01209-0, 2021.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Herold, M., Latham, J. S., Di Gregorio, A., and Schmullius, C. C.: Evolving standards in land cover characterization, J. Land Use Sci., 1, 157–168, https://doi.org/10.1080/17474230601079316, 2006.
Hu, J., Zhang, B., Peng, D., Huang, J., Zhang, W., Zhao, B., Li, Y., Cheng, E., Lou, Z., Liu, S., Yang, S., Tan, Y., and Lv, Y.: Mapping 10 m harvested area in the major winter wheat-producing regions of China from 2018 to 2022, Sci. Data, 11, 1038, https://doi.org/10.1038/s41597-024-03867-z, 2024.
Ibrahim, R. L., Al-Mulali, U., Ajide, K. B., Mohammed, A., and Al-Faryan, M. A. S.: The implications of food security on sustainability: do trade facilitation, population growth, and institutional quality make or mar the target for SSA?, Sustainability-Basel, 15, 2089, https://doi.org/10.3390/su15032089, 2023.
Jayne, T. S., Chamberlin, J., and Headey, D. D.: Land pressures, the evolution of farming systems, and development strategies in Africa: A synthesis, Food Policy, 48, 1–17, https://doi.org/10.1016/j.foodpol.2014.05.014, 2014.
Jin, H., Stehman, S. V., and Mountrakis, G.: Assessing the impact of training sample selection on accuracy of an urban classification: a case study in Denver, Colorado, Int. J. Remote Sens., 35, 2067–2081, https://doi.org/10.1080/01431161.2014.885152, 2014.
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., and Brumby, S. P.: Global land use/land cover with Sentinel 2 and deep learning, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021m 4704–4707, https://doi.org/10.1109/IGARSS47720.2021.9553499, 2021.
Kehoe, L., Romero-Muñoz, A., Polaina, E., Estes, L., Kreft, H., and Kuemmerle, T.: Biodiversity at risk under future cropland expansion and intensification, Nat. Ecol. Evol., 1, 1129–1135, https://doi.org/10.1038/s41559-017-0234-3, 2017.
Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I.: How accurate are existing land cover maps for agriculture in sub-saharan africa?, Sci. Data, 11, 486, https://doi.org/10.1038/s41597-024-03306-z, 2024a.
Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I.: Comparison of cropland maps derived from land cover maps in sub-saharan africa, Zenodo [data set], https://doi.org/10.5281/zenodo.10694610, 2024b.
Lana-Renault, N., Romero, E. N., Cammeraat, E., and Llorente, J. Á.: Impact of farmland abandonment on water resources and soil conservation, MDPI, https://doi.org/10.3390/books978-3-03936-612-5, 2020.
Laris, P., Koné, M., Dembélé, F., Rodrigue, C. M., Yang, L., Jacobs, R., Laris, Q., and Camara, F.: The Pyrogeography of Methane Emissions from Seasonal Mosaic Burning Regimes in a West African Landscape, Fire, 6, 52, https://doi.org/10.3390/fire6020052, 2023.
Laso Bayas, J. C., Lesiv, M., Waldner, F., Schucknecht, A., Duerauer, M., See, L., Fritz, S., Fraisl, D., Moorthy, I., McCallum, I., Perger, C., Danylo, O., Defourny, P., Gallego, J., Gilliams, S., Akhtar, I. u. H., Baishya, S. J., Baruah, M., Bungnamei, K., Campos, A., Changkakati, T., Cipriani, A., Das, K., Das, K., Das, I., Davis, K. F., Hazarika, P., Johnson, B. A., Malek, Z., Molinari, M. E., Panging, K., Pawe, C. K., Pérez-Hoyos, A., Sahariah, P. K., Sahariah, D., Saikia, A., Saikia, M., Schlesinger, P., Seidacaru, E., Singha, K., and Wilson, J. W.: A global reference database of crowdsourced cropland data collected using the geo-wiki platform, Sci. Data, 4, 170136, https://doi.org/10.1038/sdata.2017.136, 2017.
Liu, L., Zhang, X., and Zhao, T.: GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022, Zenodo [data set], https://doi.org/10.5281/zenodo.8239305, 2023.
Lou, Z., Peng, D., Shi, Z., Wang, Y., Zhang, Y., Yan, X., Chen, Z., Ye, S., Yu, L., Hu, J., Lv, Y., Peng, H., Zhang, Y., and Zhang, B.: AFCD: a 30 m resolution annual cropland extent dataset of Africa in recent decades of the 21st century, Zenodo [data set], https://doi.org/10.5281/zenodo.14920706, 2025.
Mallet, M., Voldoire, A., Solmon, F., Nabat, P., Drugé, T., and Roehrig, R.: Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol coupled climate model, Atmos. Chem. Phys., 24, 12509–12535, https://doi.org/10.5194/acp-24-12509-2024, 2024.
Mano, Y., Takahashi, K., and Otsuka, K.: Mechanization in land preparation and agricultural intensification: the case of rice farming in the cote d'ivoire, Agr. Econ., 51, 899–908, https://doi.org/10.1111/agec.12599, 2020.
Nabil, M., Zhang, M., Wu, B., Bofana, J., and Elnashar, A.: Constructing a 30 m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets, Big Earth Data, 6, 54–76, https://doi.org/10.1080/20964471.2021.1914400, 2022.
Nilsson, P.: The role of land use consolidation in improving crop yields among farm households in Rwanda, J. Dev. Stud., 55, 1726–1740, https://doi.org/10.1080/00220388.2018.1520217, 2019.
Pereira, P., Brevik, E., and Trevisani, S.: Mapping the environment, Sci. Total Environ., 610–611, 17–23, https://doi.org/10.1016/j.scitotenv.2017.08.001, 2018.
Phalke, A. R., Özdoğan, M., Thenkabail, P. S., Erickson, T., Gorelick, N., Yadav, K., and Congalton, R. G.: Mapping croplands of Europe, middle east, russia, and central asia using landsat, random forest, and google earth engine, ISPRS J. Photogramm., 167, 104–122, https://doi.org/10.1016/j.isprsjprs.2020.06.022, 2020.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022.
Ramo, R., Roteta, E., Bistinas, I., van Wees, D., Bastarrika, A., Chuvieco, E., and van der Werf, G. R.: African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data, P. Natl. Acad. Sci. USA, 118, e2011160118, https://doi.org/10.1073/pnas.2011160118, 2021.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of landsat-7 to landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Schneider, J. M., Delzeit, R., Neumann, C., Heimann, T., Seppelt, R., Schuenemann, F., Söder, M., Mauser, W., and Zabel, F.: Effects of profit-driven cropland expansion and conservation policies, Nat. Sustain., 7, 1335–1347, https://doi.org/10.1038/s41893-024-01410-x, 2024.
Searchinger, T. D., Estes, L., Thornton, P. K., Beringer, T., Notenbaert, A., Rubenstein, D., Heimlich, R., Licker, R., and Herrero, M.: High carbon and biodiversity costs from converting Africa's wet savannahs to cropland, Nat. Clim. Change, 5, 481–486, https://doi.org/10.1038/nclimate2584, 2015.
See, L.: A global reference database of crowdsourced cropland data collected using the geo-wiki platform, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.873912, 2017.
Song, W., Prishchepov, A. V., and Song, W.: Mapping the spatial and temporal patterns of fallow land in mountainous regions of China, Int. J. Digit. Earth, 15, 2148–2167, https://doi.org/10.1080/17538947.2022.2148765, 2022.
Stephens, E. C., Jones, A. D., and Parsons, D.: Agricultural systems research and global food security in the 21st century: an overview and roadmap for future opportunities, Agr. Syst., 163, 1–6, https://doi.org/10.1016/j.agsy.2017.01.011, 2018.
Teluguntla, P., Thenkabail, P., Oliphant, A., Gumma, M., Aneece, I., Foley, D., and McCormick, R.: Landsat-Derived Global Rainfed and Irrigated-Cropland Product 30 m V001, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/COMMUNITY/LGRIP/LGRIP30.001, 2023.
Thenkabail, P. S., Teluguntla, P. G., Xiong, J., Oliphant, A., Congalton, R. G., Ozdogan, M., Gumma, M. K., Tilton, J. C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D.: Global cropland-extent product at 30 m resolution (GCEP30) derived from landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google earth engine cloud, Professional Paper, U. S. Geological Survey, https://doi.org/10.3133/pp1868, 2021.
Traba, J. and Morales, M. B.: The decline of farmland birds in Spain is strongly associated to the loss of fallowland, Sci. Rep.-UK, 9, 9473, https://doi.org/10.1038/s41598-019-45854-0, 2019.
Tubiello, F. N., Conchedda, G., Casse, L., Hao, P., De Santis, G., and Chen, Z.: A new cropland area database by country circa 2020, Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, 2023a.
Tubiello, F. N., Conchedda, G., Casse, L., Pengyu, H., Zhongxin, C., De Santis, G., Fritz, S., and Muchoney, D.: Measuring the world's cropland area, Nat. Food, 4, 30–32, https://doi.org/10.1038/s43016-022-00667-9, 2023b.
Van De Kerchove, R., Zanaga, D., Keersmaecker, W., Souverijns, N., Wevers, J., Brockmann, C., Grosu, A., Paccini, A., Cartus, O., and Santoro, M.: ESA WorldCover: Global land cover mapping at 10 m resolution for 2020 based on Sentinel-1 and 2 data, AGU Fall Meeting Abstracts, 2021, GC45I-0915, https://ui.adsabs.harvard.edu/abs/2021AGUFMGC45I0915V (last access: 31 July 2025), 2021.
Vermote, E.: LEDAPS surface reflectance product description, https://www.usgs.gov/media/files/landsat-4-7-collection-1-surface-reflectance-code-ledaps-product-guide (last access: 31 July 2025), 2007.
Vermote, E. F. and Kotchenova, S.: Atmospheric correction for the monitoring of land surfaces, J. Geophys. Res.-Atmos., 113, D23S90, https://doi.org/10.1029/2007JD009662, 2008.
Waldner, F., Fritz, S., Di Gregorio, A., and Defourny, P.: Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps, Remote Sens.-Basel, 7, 7959–7986, https://doi.org/10.3390/rs70607959, 2015.
Xian, G. Z., Smith, K., Wellington, D., Horton, J., Zhou, Q., Li, C., Auch, R., Brown, J. F., Zhu, Z., and Reker, R. R.: Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product, Earth Syst. Sci. Data, 14, 143–162, https://doi.org/10.5194/essd-14-143-2022, 2022.
Xiao, L., Wang, G., Wang, E., Liu, S., Chang, J., Zhang, P., Zhou, H., Wei, Y., Zhang, H., Zhu, Y., Shi, Z., and Luo, Z.: Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production, Nat. Food, 1–13, https://doi.org/10.1038/s43016-023-00891-x, 2024.
Xie, Y., Spawn-Lee, S. A., Radeloff, V. C., Yin, H., Robertson, G. P., and Lark, T. J.: Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses, Environ. Res. Lett., 19, 44009, https://doi.org/10.1088/1748-9326/ad2d12, 2024.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of continental africa using google earth engine cloud computing, ISPRS J. Photogramm., 126, 225–244, https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017a.
Xiong, J., Thenkabail, P. S., Tilton, J. C., Gumma, M. K., Teluguntla, P., Oliphant, A., Congalton, R. G., Yadav, K., and Gorelick, N.: Nominal 30 m cropland extent map of continental africa by integrating pixel-based and object-based algorithms using sentinel-2 and landsat-8 data on Google earth engine, Remote Sens.-Basel, 9, 1065, https://doi.org/10.3390/rs9101065, 2017b.
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., Ramoino, F., and Arino, O.: ESA WorldCover 10 m 2020 v100 (v100), Zenodo [data set], https://doi.org/10.5281/zenodo.5571936, 2021.
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F., and Arino, O.: ESA WorldCover 10 m 2021 v200 (v200), Zenodo [data set], https://doi.org/10.5281/zenodo.7254221, 2022.
Zhang, C., Dong, J., and Ge, Q.: Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products, Sci. Data, 9, 407, https://doi.org/10.1038/s41597-022-01522-z, 2022.
Zhang, G., Xiao, X., Dong, J., Kou, W., Jin, C., Qin, Y., Zhou, Y., Wang, J., Menarguez, M. A., and Biradar, C.: Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data, ISPRS J. Photogramm., 106, 157–171, https://doi.org/10.1016/j.isprsjprs.2015.05.011, 2015.
Zhang, H., Lou, Z., Peng, D., Zhang, B., Luo, W., Huang, J., Zhang, X., Yu, L., Wang, F., Huang, L., Liu, G., Gao, S., Hu, J., Yang, S., and Cheng, E.: Mapping annual 10 m soybean cropland with spatiotemporal sample migration, Sci. Data, 11, 439, https://doi.org/10.1038/s41597-024-03273-5, 2024a.
Zhang, X., Liu, L., Chen, X., Xie, S., and Gao, Y.: Fine land-cover mapping in China using landsat datacube and an operational SPECLib-based approach, Remote Sens.-Basel, 11, 1056, https://doi.org/10.3390/rs11091056, 2019.
Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X., and Liu, L.: GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method, Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, 2024b.
Zhu, Z. and Woodcock, C. E.: Continuous change detection and classification of land cover using all available landsat data, Remote Sens. Environ., 144, 152–171, https://doi.org/10.1016/j.rse.2014.01.011, 2014.
Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Olofsson, P., Loveland, T. R., Jin, S., Dahal, D., Yang, L., and Auch, R. F.: Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative, ISPRS J. Photogramm., 122, 206–221, https://doi.org/10.1016/j.isprsjprs.2016.11.004, 2016.
Zumkehr, A. and Campbell, J. E.: Historical U. S. cropland areas and the potential for bioenergy production on abandoned croplands, Environ. Sci. Technol., 47, 3840–3847, https://doi.org/10.1021/es3033132, 2013.
Zuo, J., Zhang, L., Xiao, J., Chen, B., Zhang, B., Hu, Y., Mamun, M. M. A. A., Wang, Y., and Li, K.: GCL_FCS30: a global coastline dataset with 30 m resolution and a fine classification system from 2010 to 2020, Sci. Data, 12, 129, https://doi.org/10.1038/s41597-025-04430-0, 2025.
Short summary
This study creates the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over 2 decades, while 11.5 % of cropland was abandoned by 2018, revealing hidden challenges in agricultural sustainability. These yearly field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
This study creates the first detailed annual maps of Africa's cropland extent from 2000 to 2022...
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