Dataset List


Data Details

Global Cultivatable Land Suitability Dataset Based on Physical-geographic Factors

ZHANG Chengpeng1YE Yu*1FANG Xiuqi1
1 Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China


Published:Apr. 2022

Visitors:1702       Data Files Downloaded:91      
Data Downloaded:323.21 MB      Citations:

Key Words:

physical-geographic factors,global,cultivatable land suitability,spatial differentiation


The cultivatable land suitability terms as degree of land suitable for cultivations. The authors take 0.5° x 0.5° grids of the world as the analysis basic units, 5′ x 5′ as spatial resolution. The global cultivatable land suitability dataset based on physical-geographic factors was developed by integrating 13 physical-geographical factors, which affects the cultivation intensity, such as climate, soil, topography and etc, with Pearson correlation analysis. The data result indicates that the spatial distribution pattern of cultivatable land suitability is basically consistent with the value of reclamation rate. That is, in the main agricultural areas in the world (such as plains in Eastern European, North China, Ganges, Central North America, etc.), it generally shows a high reclamation intensity, while in areas with relatively extremely conditions for cultivation, the value of cultivatable land suitability is generally very low. The dataset is archived in .img format, and consists of 4 data files with data size of 38.7 MB (compressed to one file with 3.55 MB).

Foundation Item:

Ministry of Science and Technology of P. R. China (2017YFA0603304);

Data Citation:

ZHANG Chengpeng, YE Yu*, FANG Xiuqi. Global Cultivatable Land Suitability Dataset Based on Physical-geographic Factors[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022.


[1] Ellis, E. C., Kaplan, J. O., Fuller, D. Q., et al. Used planet: A global history [J]. Proceedings of the National Academy of Sciences, 2013, 110(20): 7978-7985.
     [2] Foley, J. O., DeFries, R., Asner, G. P., et al. Global consequences of land use [J]. Science, 2005, 309(5734): 570-574.
     [3] Gaillard, M. J. LandCover6k: Global anthropogenic land-cover change and its role in past climate [M]. PAGES Magazine, 2015, 23(1): 38-39.
     [4] Lambin, E. F., Geist, H. J. Land-use and land-cover change: Local processes and global impacts [M]. Berlin: Springer Science & Business Media, 2008.
     [5] Goldewijk, K. K., Beusen, A., Doelman, J., et al. Anthropogenic land use estimates for the Holocene–HYDE 3.2 [J]. Earth System Science Data, 2017, 9(2): 927-953.
     [6] Moran, E., Ojima, D. S., Buchmann, B., et al. Global Land Project: science plan and implementation strategy [M]. Stockholm: IGBP Secretariat, 2005.
     [7] Ramankutty, N., Foley, J. A., Hall, F. G., et al. ISLSCP II historical croplands cover, 1700-1992 [DB/OL]. ORNL DAAC, 2010.
     [8] Ramankutty, N., Foley, J. A. Estimating historical changes in global land cover: Croplands from 1700 to 1992 [J]. Global Biogeochemical Cycles, 1999, 13(4): 997-1027.
     [9] Pongratz, J., Reick, C., Raddatz, T., et al. A reconstruction of global agricultural areas and land cover for the last millennium [J]. Global Biogeochemical Cycles, 2008, 22(6): 1-16.
     [10] Kaplan, J. O., Krumhardt, K. M., Ellis, E. C., et al. Holocene carbon emissions as a result of anthropogenic land cover change [J]. The Holocene, 2011, 21(5): 775-791.
     [11] Boyle, J. F., Gaillard, M. J., Kaplan, J. O., et al. Modelling prehistoric land use and carbon budgets: A critical review [J]. The Holocene, 2011, 21(5): 1-8.
     [12] Pielke, R. A., Pitman, A., Niyogi, D., et al. Land use/land cover changes and climate: Modeling analysis and observational evidence [J]. Wiley Interdisciplinary Reviews: Climate Change, 2011, 2(6): 828-850.
     [13] Ge, Q. S., Dai, J. H., He, F. N., et al. Numerical changes and driving factor of provincial cropland resources in China over the past 300 years [J]. Natural Resources Advance, 2003, 13(8): 825-832.
     [14] Lin, S. S., Zheng, J. Y., He, F. N. The approach for gridding data derived from historical cropland records of the traditional cultivated region in China [J]. Acta Geographica Sinica, 2008, 61(1): 83-92.
     [15] Ye, Y., Fang, X. Q., Ren, Y. Y., et al. Reconstruction of cropland cover changes in the Northeast China over the past 300 years [J]. Science China: D Series, 2009, 39(3): 340-350.
     [16] He, F. N., Li, S. C., Zhang, X. Z., et al. Comparisons of cropland area from multiple datasets over the past 300 years in the traditional cultivated region of China [J]. Journal of Geographical Sciences, 2013, 23(6): 978-990.
     [17] Li, S. C., He, F. N., Zhang, X. Z. A spatially explicit reconstruction of cropland cover in China from 1661 to 1996 [J]. Regional Environmental Change, 2016, 16(2): 417-428.
     [18] Yang, X. H., Jin, X. B., Guo, B. B., et al. Research on reconstructing spatial distribution of historical cropland over 300 years in traditional cultivated regions of China [J]. Global and Planetary Change, 2015, 128: 90-102.
     [19] Fick, S. E., Hijmans, R. J. WorldClim2: New 1km spatial resolution climate surfaces for global land areas [J]. International Journal of Climatology, 2017, 37(12): 4302-4315.
     [20] Danielson, J. J., Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010) [M]. Washington, DC, USA: US Department of the Interior, US Geological Survey, 2011.
     [21] Pinzon, J. E., Tucker, C. J. A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series [J]. Remote Sensing, 2014, 6(8): 6929-6960.
     [22] Hengl, T., Mendes, J. J., Heuvelink, G. B., et al. SoilGrids250m: Global gridded soil information based on machine learning [J]. PLoS ONE, 2017, 12: e0169748.
     [23] Zhang, C. P., Ye, Y., Fang, X. Q., et al. Synergistic modern global 1 km cropland dataset derived from multi-sets of land cover products [J]. Remote Sensing, 2019, 11(19): 1-18.

Data Product:

ID Data Name Data Size Operation
1 GlobalCultivLandSuitability.rar 3636.98KB