{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T00:36:05Z","timestamp":1793234165521,"version":"4.5.0"},"reference-count":60,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["2017YFE0104600"],"award-info":[{"award-number":["2017YFE0104600"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Maps of different kinds of crops offer information about both crop distribution and crop mix, which support analyses on food security, environmental change, and climate change. Despite the growing capability for mapping specific crops, the majority of studies have focused on a few dominant crops, whereas maps with a greater diversity of crops lack research. Combining cropping seasons derived from MODIS EVI data, regional crop calendar data, and agricultural statistical surveys, we developed an allocation model to map 14 major crops at a 1 km resolution across China for the years 2000, 2010, and 2015. The model was verified based on the fitness between the area of the three typical combinations of region, crop\/crop group derived from remote sensing data, and statistical data. The R2, indicating fitness, ranged from 0.51 to 0.75, with a higher value for the crops distributed in plain regions and a lower value in regions with topographically diverse landscapes. Within the same combination of region and crop\/crop group, the larger harvest area a province has, the higher its fitness, suggesting an overall reliable result at the national level. A comparison of paddy rice between our results and the National Land Use\/Cover Database of China showed a relatively high R2 and slope of fitness (0.67 and 0.71, respectively). Compared with the commonly used average allocation model, and without lending cropping season information, the diversity index of the results from our model is about 30% higher, indicating crop maps with greater spatial details. According to the spatial distribution analysis of the four main crops, the grids showing decreased trends accounted for 74.92%, 57.32%, and 59.00% of the total changed grid for wheat, rice, and soybean crops, respectively, while accounting for only 37.71% for maize. The resulting data sets can be used to improve assessments for nutrient security and sustainability of cropping systems, as well as their resilience in a changing climate.<\/jats:p>","DOI":"10.3390\/rs14081800","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T05:13:08Z","timestamp":1649481188000},"page":"1800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Mapping Crop Distribution Patterns and Changes in China from 2000 to 2015 by Fusing Remote-Sensing, Statistics, and Knowledge-Based Crop Phenology"],"prefix":"10.3390","volume":"14","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Zuo","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global Consequences of Land Use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6868","DOI":"10.1073\/pnas.1117054109","article-title":"Global changes in diets and the consequences for land requirements for food","volume":"109","author":"Kastner","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1126\/science.1246067","article-title":"Leverage points for improving global food security and the environment","volume":"345","author":"West","year":"2014","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1038\/s41893-018-0076-2","article-title":"Progress towards sustainable intensification in China challenged by land-use change","volume":"1","author":"Zuo","year":"2018","journal-title":"Nat. Sustain."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/nature15743","article-title":"Managing nitrogen for sustainable development","volume":"528","author":"Zhang","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2022, February 15). World Population Prospects 2019. Online Edition. Rev. 1. Available online: https:\/\/population.un.org\/wpp\/."},{"key":"ref_8","unstructured":"FAOSTAT (2020, July 09). FAOSTAT Online Statistical Service. Available online: http:\/\/www.fao.org\/faostat\/en\/#data."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9326","DOI":"10.1073\/pnas.1701762114","article-title":"Temperature increase reduces global yields of major crops in four independent estimates","volume":"114","author":"Zhao","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1080\/01431161.2016.1194545","article-title":"Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity","volume":"37","author":"Waldner","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","unstructured":"FAO (2022, February 15). Food and Agriculture Organization of the United Nations. Available online: http:\/\/www.fao.org\/."},{"key":"ref_12","unstructured":"NBS (2020, July 09). China National Statistical Yearbook, Available online: http:\/\/www.stats.gov.cn\/english\/."},{"key":"ref_13","first-page":"840","article-title":"Spatio-temporal changes in Chinese crop patterns over the past three decades","volume":"71","author":"Liu","year":"2016","journal-title":"Acta Geogr. Sin."},{"key":"ref_14","first-page":"1","article-title":"Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000","volume":"2","author":"Monfreda","year":"2008","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., and Li, R. (2022). Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14041004"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"1","article-title":"Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data","volume":"58","author":"Zhu","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2016.12.002","article-title":"Energy crop mapping with enhanced TM\/MODIS time series in the BCAP agricultural lands","volume":"124","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16091","DOI":"10.3390\/rs71215820","article-title":"Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1109\/JSTARS.2016.2560141","article-title":"Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data","volume":"9","author":"Kussul","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3934\/geosci.2017.2.163","article-title":"Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale","volume":"3","author":"Skakun","year":"2017","journal-title":"AIMS Geosci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Bezner Kerr, R., Lupafya, E., and Dakishoni, L. (2021). Crop type and land cover mapping in northern Malawi using the integration of sentinel-1, sentinel-2, and planetscope satellite data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.007","article-title":"Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010","volume":"91","author":"Gumma","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","first-page":"659","article-title":"Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series","volume":"55","author":"Onojeghuo","year":"2018","journal-title":"GIS Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.rse.2011.10.011","article-title":"Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index","volume":"119","author":"Pan","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hou, J., Huang, C., Zhang, Y., and Li, X. (2021). Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest. Remote Sens., 13.","DOI":"10.3390\/rs13152988"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111951","DOI":"10.1016\/j.rse.2020.111951","article-title":"Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2016.05.014","article-title":"Automated mapping of soybean and corn using phenology","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ofori-Ampofo, S., Pelletier, C., and Lang, S. (2021). Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13224668"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"094023","DOI":"10.1088\/1748-9326\/ab3b93","article-title":"Probabilistic global maps of crop-specific areas from 1961 to 2014","volume":"14","author":"Jackson","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.agsy.2014.01.002","article-title":"Generating global crop distribution maps: From census to grid","volume":"127","author":"You","year":"2014","journal-title":"Agric. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"085197","DOI":"10.1117\/1.JRS.8.085197","article-title":"Spatial evaluation of crop maps by the spatial production allocation model in China","volume":"8","author":"Tan","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_39","first-page":"36","article-title":"Disaggregating and mapping crop statistics using hypertemporal remote sensing","volume":"12","author":"Khan","year":"2010","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2014.04.004","article-title":"A 2010 update of National Land Use\/Cover Database of China at 1:100,000 scale using medium spatial resolution satellite images","volume":"149","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112365","DOI":"10.1016\/j.rse.2021.112365","article-title":"Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates","volume":"258","author":"Hu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"CAAS (2018, March 07). AgriData, (Chinese Academy of Agricultural Sciences, CAAS, 2014). Available online: http:\/\/www.agridata.cn\/default.aspx."},{"key":"ref_43","unstructured":"NBSC (2018, July 12). National Data: Online Statistical Service, (National Bureau of Statistics of the People\u2019s Republic of China 2017), Available online: http:\/\/data.stats.gov.cn\/index.htm."},{"key":"ref_44","unstructured":"T\u00f3th, G., Kozlowski, B., Prieler, S., and Wiberg, D. (2019, November 15). Global Agro-ecological Zones. Available online: https:\/\/www.gaez.iiasa.ac.at\/docs\/GAEZ_User_Guide.pdf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/j.ijleo.2015.10.107","article-title":"Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas","volume":"127","author":"Muhammad","year":"2016","journal-title":"Optik"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/S2095-3119(13)60308-1","article-title":"Spatial Exploration of Multiple Cropping Efficiency in China Based on Time Series Remote Sensing Data and Econometric Model","volume":"12","author":"Zuo","year":"2013","journal-title":"J. Integr. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., Li, R., Bian, X., Wang, R., and Yu, C. (2021). Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090587"},{"key":"ref_49","unstructured":"Liu, X. (1982). Farming System, China Agriculture Press. (In Chinese)."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","first-page":"133","article-title":"Mapping croplands, cropping patterns, and crop types using MODIS time-series data","volume":"69","author":"Chen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105595","DOI":"10.1016\/j.compag.2020.105595","article-title":"Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices","volume":"175","author":"Feyisa","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1080\/17538947.2016.1267269","article-title":"Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000\u20132015) data","volume":"10","author":"Teluguntla","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2012.11.009","article-title":"Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data","volume":"130","author":"Brown","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.isprsjprs.2016.07.007","article-title":"Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data","volume":"119","author":"Chen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"63","article-title":"Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"141","article-title":"Multiple cropping index of Northern China based on MODIS\/EVI","volume":"25","author":"Zuo","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1016\/S2095-3119(19)62812-1","article-title":"Early-season crop type mapping using 30-m reference time series","volume":"19","author":"Hao","year":"2020","journal-title":"J. Integr. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:25Z","timestamp":1760136625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":60,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081800"],"URL":"https:\/\/doi.org\/10.3390\/rs14081800","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]}}}