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Global LULC Projection Dataset under SSP-RCP Scenarios at 1-km Resolution (2020-2100)

Tianyuan Zhang1Changxiu Cheng*1,2Xudong Wu*3,4
1 State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing,100875,PR China2 National Tibetan Plateau Data Center,Beijing,100101,PR China3 School of Soil and Water Conservation,Beijing Forestry University,Beijing,100083,PR China4 Research Department of Complexity Science,Potsdam Institute for Climate Impact Research,Potsdam,14473,Germany


Published:Jan. 2024

Visitors:1441       Data Files Downloaded:62      
Data Downloaded:41626.29 MB      Citations:

Key Words:

land use and land cover (LULC),spatial heterogeneity,land use simulation,cellular automata,high-resolution dataset


This land use and land cover (LULC) projection dataset under SSP-RCP scenarios is on a global scale with a resolution of 1 km grid and encompasses a timespan from 2020 to 2100. Based on the ESA-CCI historical LULC data, the authors first use the GCAM model to predict the future LULC area demand, then use an improved cellular automata model–PLUS model to downscale the demand and iteratively simulate the high spatial resolution dataset. The dataset achieves the simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10), precisely capturing the spatial-temporal heterogeneity and patch aggregation of global LULC changes under the combined effects of climate change and socio-economic development. The dataset include: (1) simulated LULC data in 2020; (2) predicted LULC data in four periods (2030, 2050, 2070, 2100) under five SSP-RCP scenarios. These data are projected in the world-Mercator projection coordinate system and provided in single-band GeoTIFF format. The simulated data files follow a standardized naming convention “sspx_pp_yyyy.tif”, where x represents the simulated SSPs scenario (1 to 5), pp represents the simulated RCPs scenario, and yyyy represents the simulated year. Each GeoTIFF data file includes integer raster attribute values ranging from 1 to 6, which represent the following land use types: cropland, forest, grassland, urban and rural industrial and mining residential land, barren, and water. The dataset is archived in .tif and .txt formats, consisting of 22 files with a data volume of 1.85 GB (compressed to one file with 671 MB). The paper associated with this dataset was published in Scientific Data, issue 10, 2023.

Foundation Item:

National Natural Science Foundation of China (42041007, 71904003); Alexander von Humboldt Foundation (Recipient: Xudong Wu); Chinese Academy of Sciences (XDA23100303); China Association for Science and Technology (20202022QNRC002)

Data Citation:

Tianyuan Zhang, Changxiu Cheng*, Xudong Wu*. Global LULC Projection Dataset under SSP-RCP Scenarios at 1-km Resolution (2020-2100)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024.


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     [4] Chen, M., Vernon, C. R., Graham, N. T., et al. Global land use for 2015–2100 at 0.05 resolution under diverse socioeconomic and climate scenarios[J]. Scientific Data, 2020, 7(1): 320.
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     [6] Hurtt, G. C., Chini, L., Sahajpal, R., et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6[J]. Geoscientific Model Development, 2020, 13(11): 5425-5464.
     [7] Li, X., Chen, G. Z., Liu, X. P., et al. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human–environment interactions[J]. Annals of the American Association of Geographers, 2017, 107(5): 1040-1059.
     [8] Liang, X., Guan, Q. F., Clarke, K. C., et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China[J]. Computers, Environment and Urban Systems, 2021, 85: 101569.
     [9] Luo, M., Hu, G. H., Chen, G. Z., et al. 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100[J]. Scientific data, 2022, 9(1): 110.
     [10] Luo, M. Simulation and analysis of 1km land use and land cover change in China under full SSP-RCP scenarios based on GCAM-FLUS models[D/OL]. Shanghai: East China Normal University, 2021.

Data Product:

ID Data Name Data Size Operation
1 Global_1km_LULC_2020_2100.rar 687505.15KB