Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2297-2024
© Author(s) 2024. 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-16-2297-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 May 2024
Data description paper |
| 06 May 2024
| 06 May 2024
A 30 m annual cropland dataset of China from 1986 to 2021
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Shengbiao Wu
Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong SAR 999077, China
Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong SAR 999077, China
Urban Systems Institute and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR 999077, China
HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR 999077, China
Qihao Weng
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China
Yuqi Bai
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Jun Yang
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Bing Xu
CORRESPONDING AUTHOR
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
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Cited articles
Belgiu, M. and Csillik, O.: Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sens. Environ, 204, 509–523, https://doi.org/10.1016/j.rse.2017.10.005, 2018.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program, Geocarto Int., 26, 341–358, https://doi.org/10.1080/10106049.2011.562309, 2011.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Bryan, B. A., Gao, L., Ye, Y., Sun, X., Connor, J. D., Crossman, N. D., Stafford-Smith, M., Wu, J., He, C., Yu, D., Liu, Z., Li, A., Huang, Q., Ren, H., Deng, X., Zheng, H., Niu, J., Han, G., and Hou, X.: China's response to a national land-system sustainability emergency, Nature, 559, 193–204, https://doi.org/10.1038/s41586-018-0280-2, 2018.
Canny, J.: A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 679–698, https://doi.org/10.1109/TPAMI.1986.4767851, 1986.
Chen, B., Tu, Y., Song, Y., Theobald, D. M., Zhang, T., Ren, Z., Li, X., Yang, J., Wang, J., Wang, X., Gong, P., Bai, Y., and Xu, B.: Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America, ISPRS J. Photogramm., 178, 203–218, https://doi.org/10.1016/j.isprsjprs.2021.06.010, 2021.
Crist, E. P.: A TM Tasseled Cap equivalent transformation for reflectance factor data, Remote Sens. Environ., 17, 301–306, https://doi.org/10.1016/0034-4257(85)90102-6, 1985.
Cui, K. and Shoemaker, S. P.: A look at food security in China, npj Sci. Food, 2, 4, https://doi.org/10.1038/s41538-018-0012-x, 2018.
d'Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., and van der Velde, M.: From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations, Remote Sens. Environ., 266, 112708, https://doi.org/10.1016/j.rse.2021.112708, 2021.
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.
Defourny, P., Jarvis, I., and Blaes, X.: JECAM Guidelines for cropland and crop type definition and field data collection, JECAM, https://jecam.org/wp-content/uploads/2018/10/JECAM_Guidelines_for_Field_Data_Collection_v1_0.pdf (last access: 1 May 2024), 2014.
Di Gregorio, A.: Land cover classification system: classification concepts and user manual: LCCS, Food & Agriculture Org, https://www.fao.org/4/x0596e/x0596e00.htm (last access: 1 May 2024), 2005.
FAO: FAOSTAT, Methods & Standards, https://www.fao.org/statistics/methods-and-standards/general/en (last access: 1 May 2024), 2016.
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.
Gao, X., Cheng, W., Wang, N., Liu, Q., Ma, T., Chen, Y., and Zhou, C.: Spatio-temporal distribution and transformation of cropland in geomorphologic regions of China during 1990–2015, J. Geograph. Sci., 29, 180–196, https://doi.org/10.1007/s11442-019-1591-4, 2019.
Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., and Hasanlou, M.: Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples, ISPRS J. Photogramm., 167, 276–288, https://doi.org/10.1016/j.isprsjprs.2020.07.013, 2020.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., and Toulmin, C.: Food Security: The Challenge of Feeding 9 Billion People, Science, 327, 812–818, https://doi.org/10.1126/science.1185383, 2010.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607–2654, https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song, L.: Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017, Sci. Bull., 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Gumma, M. K., Thenkabail, P. S., Maunahan, A., Islam, S., and Nelson, A.: Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010, ISPRS J. Photogramm., 91, 98–113, https://doi.org/10.1016/j.isprsjprs.2014.02.007, 2014.
Ho, T. K.: Random decision forests, Proceedings of 3rd international conference on document analysis and recognition, 278–282, https://doi.org/10.1109/ICDAR.1995.598994, 1995.
Huang, C., Davis, L. S., and Townshend, J. R. G.: An assessment of support vector machines for land cover classification, Int. J. Remote Sens., 23, 725–749, https://doi.org/10.1080/01431160110040323, 2002.
Huang, H., Wang, J., Liu, C., Liang, L., Li, C., and Gong, P.: The migration of training samples towards dynamic global land cover mapping, ISPRS J. Photogramm., 161, 27–36, https://doi.org/10.1016/j.isprsjprs.2020.01.010, 2020.
Isbell, F., Tilman, D., Reich, P. B., and Clark, A. T.: Deficits of biodiversity and productivity linger a century after agricultural abandonment, Nature Ecol. Evol., 3, 1533–1538, https://doi.org/10.1038/s41559-019-1012-1, 2019.
Ito, J., Nishikori, M., Toyoshi, M., and Feuer, H. N.: The contribution of land exchange institutions and markets in countering farmland abandonment in Japan, Land Use Policy, 57, 582–593, https://doi.org/10.1016/j.landusepol.2016.06.020, 2016.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database, http://srtm.csi.cgiar.org (last access: 1 May 2024), 2008.
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.
Kennedy, R. E., Yang, Z., and Cohen, W. B.: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms, Remote Sens. Environ., 114, 2897–2910, https://doi.org/10.1016/j.rse.2010.07.008, 2010.
Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W. B., and Healey, S.: Implementation of the LandTrendr Algorithm on Google Earth Engine, Remote Sens., 10, 691, https://doi.org/10.3390/rs10050691, 2018.
Lambert, M.-J., Waldner, F., and Defourny, P.: Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m, Remote Sens., 8, 232, https://doi.org/10.3390/rs8030232, 2016.
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.
Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang, H., Wang, Q., Li, X., Liu, X., Xu, Y., Guo, J., Liu, C., Hackman, K. O., Zhang, M., Cheng, Y., Yu, L., Yang, J., Huang, H., and Clinton, N.: The first all-season sample set for mapping global land cover with Landsat-8 data, Sci. Bull., 62, 508–515, https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Li, Q., Liu, G., and Chen, W.: Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series, Remote Sens., 13, 5183, https://doi.org/10.3390/rs13245183, 2021.
Li, S., Li, X., Sun, L., Cao, G., Fischer, G., and Tramberend, S.: An estimation of the extent of cropland abandonment in mountainous regions of China, Land Degrad. Develop., 29, 1327–1342, https://doi.org/10.1002/ldr.2924, 2018.
Li, X., Gong, P., and Liang, L.: A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data, Remote Sens. Environ., 166, 78–90, https://doi.org/10.1016/j.rse.2015.06.007, 2015.
Li, X.-Y., Li, X., Fan, Z., Mi, L., Kandakji, T., Song, Z., Li, D., and Song, X.-P.: Civil war hinders crop production and threatens food security in Syria, Nature Food, 3, 38–46, https://doi.org/10.1038/s43016-021-00432-4, 2022.
Liang, C., Penghui, J., wei, C., Manchun, L., Liyan, W., Yuan, G., Yuzhe, P., Nan, X., Yuewei, D., and Qiuhao, H.: Farmland protection policies and rapid urbanization in China: A case study for Changzhou City, Land Use Policy, 48, 552–566, https://doi.org/10.1016/j.landusepol.2015.06.014, 2015.
Liu, D. and Cai, S.: A Spatial-Temporal Modeling Approach to Reconstructing Land-Cover Change Trajectories from Multi-temporal Satellite Imagery, Ann. Assoc. Am. Geogr., 102, 1329–1347, https://doi.org/10.1080/00045608.2011.596357, 2012.
Liu, F., Zhang, Z., Zhao, X., Wang, X., Zuo, L., Wen, Q., Yi, L., Xu, J., Hu, S., and Liu, B.: Chinese cropland losses due to urban expansion in the past four decades, Sci. Total Environ., 650, 847–857, https://doi.org/10.1016/j.scitotenv.2018.09.091, 2019.
Liu, G.: Understanding cotton cultivation dynamics in Aksu Oases (NW China) by reconstructing change trajectories using multi-temporal Landsat and Sentinel-2 data, Geocarto Int., 37, 4406–4424, https://doi.org/10.1080/10106049.2021.1886337, 2022.
Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., and Chi, W.: Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s, J. Geograph. Sci., 24, 195–210, https://doi.org/10.1007/s11442-014-1082-6, 2014.
Liu, Y., Fang, F., and Li, Y.: Key issues of land use in China and implications for policy making, Land Use Policy, 40, 6–12, https://doi.org/10.1016/j.landusepol.2013.03.013, 2014.
Lu, M., Wu, W., Zhang, L., Liao, A., Peng, S., and Tang, H.: A comparative analysis of five global cropland datasets in China, Sci. China Earth Sci., 59, 2307–2317, https://doi.org/10.1007/s11430-016-5327-3, 2016.
MacDonald, D., Crabtree, J. R., Wiesinger, G., Dax, T., Stamou, N., Fleury, P., Gutierrez Lazpita, J., and Gibon, A.: Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response, J. Environ. Manage., 59, 47–69, https://doi.org/10.1006/jema.1999.0335, 2000.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Feng, G., Kutler, J., and Teng-Kui, L.: A Landsat surface reflectance dataset for North America, 1990–2000, IEEE Geosci. Remote Sens. Lett., 3, 68–72, https://doi.org/10.1109/LGRS.2005.857030, 2006.
Maus, V., Câmara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., and Queiroz, G. R. D.: A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping, IEEE J. Sel. Top. Appl., 9, 3729–3739, https://doi.org/10.1109/JSTARS.2016.2517118, 2016.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A.: Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42–57, https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Pasquarella, V. J., Arévalo, P., Bratley, K. H., Bullock, E. L., Gorelick, N., Yang, Z., and Kennedy, R. E.: Demystifying LandTrendr and CCDC temporal segmentation, Int. J. Appl. Earth Obs., 110, 102806, https://doi.org/10.1016/j.jag.2022.102806, 2022.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Pittman, K., Hansen, M. C., Becker-Reshef, I., Potapov, P. V., and Justice, C. O.: Estimating Global Cropland Extent with Multi-year MODIS Data, Remote Sens., 2, 1844–1863, https://doi.org/10.3390/rs2071844, 2010.
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, Nature Food, 3, 19–28, 2022.
Potapov, P. V., Turubanova, S. A., Hansen, M. C., Adusei, B., Broich, M., Altstatt, A., Mane, L., and Justice, C. O.: Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data, Remote Sens. Environ., 122, 106–116, https://doi.org/10.1016/j.rse.2011.08.027, 2012.
Prishchepov, A. V., Radeloff, V. C., Dubinin, M., and Alcantara, C.: The effect of Landsat ETM/ETM+ image acquisition dates on the detection of agricultural land abandonment in Eastern Europe, Remote Sens. Environ., 126, 195–209, https://doi.org/10.1016/j.rse.2012.08.017, 2012.
Qiu, B., Li, H., Tang, Z., Chen, C., and Berry, J.: How cropland losses shaped by unbalanced urbanization process?, Land Use Policy, 96, 104715, https://doi.org/10.1016/j.landusepol.2020.104715, 2020.
Queiroz, C., Beilin, R., Folke, C., and Lindborg, R.: Farmland abandonment: threat or opportunity for biodiversity conservation? A global review, Front. Ecol. Environ., 12, 288–296, https://doi.org/10.1890/120348, 2014.
Radoux, J., Lamarche, C., Van Bogaert, E., Bontemps, S., Brockmann, C., and Defourny, P.: Automated Training Sample Extraction for Global Land Cover Mapping, Remote Sens., 6, 3965–3987, https://doi.org/10.3390/rs6053965, 2014.
Ren, C., Zhou, X., Wang, C., Guo, Y., Diao, Y., Shen, S., Reis, S., Li, W., Xu, J., and Gu, B.: Ageing threatens sustainability of smallholder farming in China, Nature, 616, 96–103, https://doi.org/10.1038/s41586-023-05738-w, 2023.
Roberts, L.: 9 Billion?, Science, 333, 540–543, https://doi.org/10.1126/science.333.6042.540, 2011.
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.
Schneibel, A., Stellmes, M., Röder, A., Frantz, D., Kowalski, B., Haß, E., and Hill, J.: Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series, Remote Sens. Environ., 195, 118–129, https://doi.org/10.1016/j.rse.2017.04.012, 2017.
Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., and Yu, T.-H.: Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change, Science, 319, 1238–1240, https://doi.org/10.1126/science.1151861, 2008.
See, L., Fritz, S., You, L., Ramankutty, N., Herrero, M., Justice, C., Becker-Reshef, I., Thornton, P., Erb, K., Gong, P., Tang, H., van der Velde, M., Ericksen, P., McCallum, I., Kraxner, F., and Obersteiner, M.: Improved global cropland data as an essential ingredient for food security, Global Food Security, 4, 37–45, https://doi.org/10.1016/j.gfs.2014.10.004, 2015.
Shao, Y. and Lunetta, R. S.: Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points, ISPRS J. Photogramm., 70, 78–87, https://doi.org/10.1016/j.isprsjprs.2012.04.001, 2012.
Tang, L., Ke, X., Chen, Y., Wang, L., Zhou, Q., Zheng, W., and Xiao, B.: Which impacts more seriously on natural habitat loss and degradation? Cropland expansion or urban expansion?, Land Degrad. Dev., 32, 946–964, https://doi.org/10.1002/ldr.3768, 2021.
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, Reston, VA, Report 1868, 63, https://doi.org/10.3133/pp1868, 2021.
Tilman, D., Balzer, C., Hill, J., and Befort, B. L.: Global food demand and the sustainable intensification of agriculture, P. Natl. Acad. Sci. USA, 108, 20260–20264, https://doi.org/10.1073/pnas.1116437108, 2011.
Traoré, F., Bonkoungou, J., Compaoré, J., Kouadio, L., Wellens, J., Hallot, E., and Tychon, B.: Using Multi-Temporal Landsat Images and Support Vector Machine to Assess the Changes in Agricultural Irrigated Areas in the Mogtedo Region, Burkina Faso, Remote Sens., 11, 1442, https://doi.org/10.3390/rs11121442, 2019.
Tu, Y., Chen, B., Zhang, T., and Xu, B.: Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach, Remote Sens., 12, 1058, https://doi.org/10.3390/rs12071058, 2020.
Tu, Y., Chen, B., Yu, L., Xin, Q., Gong, P., and Xu, B.: How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015, Landscape Ecol., 36, 243–263, https://doi.org/10.1007/s10980-020-01137-y, 2021.
Tu, Y., Wu, S., Chen, B., Weng, Q., Bai, Y., Yang, J., Yu, L., and Xu, B.: A 30 m annual cropland dataset of China from 1986 to 2021, Zenodo [data set], https://doi.org/10.5281/zenodo.7936884, 2023a.
Tu, Y., Chen, B., Yu, L., Song, Y., Wu, S., Li, M., Wei, H., Chen, T., Lang, W., Gong, P., and Xu, B.: Raveling the nexus between urban expansion and cropland loss in China, Landscape Ecol., 38, 1869–1884, https://doi.org/10.1007/s10980-023-01653-7, 2023b.
United Nations: United Nations Transforming Our World: The 2030 Agenda for Sustainable Development, Division for Sustainable Development Goals: New York, NY, USA, https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf (last access: 1 May 2024), 2015.
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, https://doi.org/10.1016/j.rse.2009.08.014, 2010.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46–56, https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Vuichard, N., Ciais, P., Belelli, L., Smith, P., and Valentini, R.: Carbon sequestration due to the abandonment of agriculture in the former USSR since 1990, Global Biogeochem. Cy., 22, GB4018, https://doi.org/10.1029/2008GB003212, 2008.
Waldner, F., Canto, G. S., and Defourny, P.: Automated annual cropland mapping using knowledge-based temporal features, ISPRS J. Photogramm., 110, 1–13, https://doi.org/10.1016/j.isprsjprs.2015.09.013, 2015a.
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., 7, 7959–7986, https://doi.org/10.3390/rs70607959, 2015b.
Wardlow, B. D. and Egbert, S. L.: Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains, Remote Sens. Environ., 112, 1096–1116, https://doi.org/10.1016/j.rse.2007.07.019, 2008.
Xie, Y., Lark, T. J., Brown, J. F., and Gibbs, H. K.: Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine, ISPRS J. Photogramm., 155, 136–149, https://doi.org/10.1016/j.isprsjprs.2019.07.005, 2019.
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, 2017.
Xu, H., Qi, S., Li, X., Gao, C., Wei, Y., and Liu, C.: Monitoring three-decade dynamics of citrus planting in Southeastern China using dense Landsat records, Int. J. Appl. Earth Obs., 103, 102518, https://doi.org/10.1016/j.jag.2021.102518, 2021.
Xu, Y., Yu, L., Zhao, F. R., Cai, X., Zhao, J., Lu, H., and Gong, P.: Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa, Remote Sens. Environ., 218, 13–31, https://doi.org/10.1016/j.rse.2018.09.008, 2018.
Xu, Y., Yu, L., Peng, D., Zhao, J., Cheng, Y., Liu, X., Li, W., Meng, R., Xu, X., and Gong, P.: Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm, Sci. China Earth Sci., 63, 1390–1407, https://doi.org/10.1007/s11430-019-9606-4, 2020.
Xue, J., Zhang, X.-l., Chen, S.-C., Hu, B.-f., Wang, N., and Shi, Z.: Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China, J. Integr. Agr., 23, 283–297, https://doi.org/10.1016/j.jia.2023.06.005, 2023.
Yan, J., Yang, Z., Li, Z., Li, X., Xin, L., and Sun, L.: Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale in Southwest China, Land Use Policy, 57, 459–469, https://doi.org/10.1016/j.landusepol.2016.06.014, 2016.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yin, H., Pflugmacher, D., Kennedy, R. E., Sulla-Menashe, D., and Hostert, P.: Mapping Annual Land Use and Land Cover Changes Using MODIS Time Series, IEEE J. Sel. Top. Appl., 7, 3421–3427, https://doi.org/10.1109/JSTARS.2014.2348411, 2014.
Yin, H., Prishchepov, A. V., Kuemmerle, T., Bleyhl, B., Buchner, J., and Radeloff, V. C.: Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series, Remote Sens. Environ., 210, 12–24, https://doi.org/10.1016/j.rse.2018.02.050, 2018.
Yin, H., Brandão, A., Buchner, J., Helmers, D., Iuliano, B. G., Kimambo, N. E., Lewińska, K. E., Razenkova, E., Rizayeva, A., Rogova, N., Spawn, S. A., Xie, Y., and Radeloff, V. C.: Monitoring cropland abandonment with Landsat time series, Remote Sens. Environ., 246, 111873, https://doi.org/10.1016/j.rse.2020.111873, 2020.
Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., and Gong, P.: FROM-GC: 30 m global cropland extent derived through multisource data integration, Int. J. Digit. Earth, 6, 521–533, 2013.
Yu, Q., Hu, Q., van Vliet, J., Verburg, P. H., and Wu, W.: GlobeLand30 shows little cropland area loss but greater fragmentation in China, Int. J. Appl. Earth Obs., 66, 37–45, https://doi.org/10.1016/j.jag.2017.11.002, 2018.
Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, W., and Václavík, T.: Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity, Nat. Commun., 10, 2844, https://doi.org/10.1038/s41467-019-10775-z, 2019.
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, 2022a.
Zhang, C., Dong, J., and Ge, Q.: Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis, Comput. Electron. Agr., 197, 106946, https://doi.org/10.1016/j.compag.2022.106946, 2022b.
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. K. and Roy, D. P.: Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification, Remote Sens. Environ., 197, 15–34, https://doi.org/10.1016/j.rse.2017.05.024, 2017.
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, 2024.
Zhang, Y., Li, X., and Song, W.: Determinants of cropland abandonment at the parcel, household and village levels in mountain areas of China: A multi-level analysis, Land Use Policy, 41, 186–192, https://doi.org/10.1016/j.landusepol.2014.05.011, 2014.
Zhong, L., Gong, P., and Biging, G. S.: Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery, Remote Sens. Environ., 140, 1–13, https://doi.org/10.1016/j.rse.2013.08.023, 2014.
Zhu, L., Liu, X., Wu, L., Tang, Y., and Meng, Y.: Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery, Remote Sens., 11, 1234, https://doi.org/10.3390/rs11101234, 2019.
Zhu, Z.: Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS J. Photogramm., 130, 370–384, https://doi.org/10.1016/j.isprsjprs.2017.06.013, 2017.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83–94, https://doi.org/10.1016/j.rse.2011.10.028, 2012.
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.
Zuo, L., Zhang, Z., Carlson, K. M., MacDonald, G. K., Brauman, K. A., Liu, Y., Zhang, W., Zhang, H., Wu, W., Zhao, X., Wang, X., Liu, B., Yi, L., Wen, Q., Liu, F., Xu, J., Hu, S., Sun, F., Gerber, J. S., and West, P. C.: Progress towards sustainable intensification in China challenged by land-use change, Nat. Sustain., 1, 304–313, https://doi.org/10.1038/s41893-018-0076-2, 2018.
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall...
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