{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:31:24Z","timestamp":1760239884991,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T00:00:00Z","timestamp":1547683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2017CFB434"],"award-info":[{"award-number":["2017CFB434"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global food demand will increase over the next few decades, and sustainable agricultural intensification on current cropland may be a preferred option to meet this demand. Mapping cropping intensity with remote sensing data is of great importance for agricultural production, food security, and agricultural sustainability in the context of global climate change. However, there are some challenges in large-scale cropping intensity mapping. First, existing indicators are too coarse, and fine indicators for measuring cropping intensity are lacking. Second, the regional, intra-class variations detected in time-series remote sensing data across vast areas represent environment-related clusters for each cropping intensity level. However, few existing studies have taken into account the intra-class variations caused by varied crop patterns, crop phenology, and geographical differentiation. In this research, we first presented a new definition, a normalized cropping intensity index (CII), to quantify cropping intensity precisely. We then proposed a Bayesian network model fusing prior knowledge (BNPK) to address the issue of intra-class variations when mapping CII over large areas. This method can fuse regional differentiation factors as prior knowledge into the model to reduce the uncertainty. Experiments on five sample areas covering the main grain-producing areas of mainland China proved the effectiveness of the model. Our research proposes the framework of obtain a CII map with both a finer spatial resolution and a fine temporal resolution at a national scale.<\/jats:p>","DOI":"10.3390\/rs11020168","type":"journal-article","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T11:30:27Z","timestamp":1547724627000},"page":"168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Jianbin","family":"Tao","sequence":"first","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis &amp; Simulation of Hubei province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis &amp; Simulation of Hubei province\/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.landusepol.2006.01.005","article-title":"Assessing farmland protection policy in China","volume":"25","author":"Lichtenberg","year":"2008","journal-title":"Land Use Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1007\/s11434-012-5235-7","article-title":"China\u2019s urban expansion from 1990 to 2010 determined with satellite remote sensing","volume":"57","author":"Wang","year":"2012","journal-title":"Chin. Sci. Bull."},{"key":"ref_3","first-page":"3","article-title":"Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s","volume":"69","author":"Liu","year":"2014","journal-title":"Acta Geogr. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.landusepol.2018.02.032","article-title":"Global cropping intensity gaps: Increasing food production without cropland expansion","volume":"76","author":"Wu","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/2013EO030006","article-title":"The need for improved maps of global cropland","volume":"94","author":"Fritz","year":"2013","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"014008","DOI":"10.1088\/1748-9326\/11\/1\/014008","article-title":"Mapping and analysing cropland use intensity from a NPP perspective","volume":"11","author":"Niedertscheider","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.apgeog.2017.01.001","article-title":"Mapping cropping intensity trends in China during 1982\u20132013","volume":"79","author":"Qiu","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s11769-013-0637-2","article-title":"Multiple cropping intensity in China derived from agro-meteorological observations and MODIS data","volume":"24","author":"Yan","year":"2014","journal-title":"Chin. Geogr. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.3390\/rs6032473","article-title":"Mapping Crop Cycles in China Using MODIS-EVI Time Series","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.ecolind.2018.04.010","article-title":"Automatic mapping afforestation, cropland reclamation and variations in cropping intensity in central east China during 2001\u20132016","volume":"91","author":"Qiu","year":"2018","journal-title":"Ecol. Indicators"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, L., Zhao, Y., Fu, Y., Pan, Y., Yu, L., and Xin, Q. (2017). High resolution mapping of cropping cycles by fusion of landsat and MODIS data. Remote Sens., 9.","DOI":"10.3390\/rs9121232"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1080\/15481603.2017.1414010","article-title":"Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series","volume":"55","author":"Biradar","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhu, W., Atzberger, C., Zhao, A., Pan, Y., and Huang, X. (2018). A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10081203"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, M., Xin, L., Li, X., Tan, M., and Wang, R. (2019). Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015. Remote Sens., 11.","DOI":"10.3390\/rs11010035"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yan, H., Liu, F., Qin, Y., Niu, Z., Doughty, R., and Xiao, X. (2018). Tracking the spatio-temporal change of cropping intensity in China during 2000\u20132015. Environ. Res. Lett.","DOI":"10.1088\/1748-9326\/aaf9c7"},{"key":"ref_16","first-page":"604","article-title":"Spatiotemporal difference and determinants of multiple cropping index in China during 1998-2012","volume":"70","author":"Xie","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8946","DOI":"10.1038\/ncomms9946","article-title":"Global biomass production potentials exceed expected future demand without the need for cropland expansion","volume":"6","author":"Mauser","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.3390\/rs2071625","article-title":"Global patterns of cropland use intensity","volume":"2","author":"Siebert","year":"2010","journal-title":"Remote Sens."},{"key":"ref_19","first-page":"748","article-title":"Progress and perspectives on agricultural remote sensing research and applications in China","volume":"20","author":"Chen","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"024015","DOI":"10.1088\/1748-9326\/11\/2\/024015","article-title":"Mapping cropland-use intensity across Europe using MODIS NDVI time series","volume":"11","author":"Estel","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/1747423X.2013.798038","article-title":"Use of Landsat and MODIS data to remotely estimate Russia\u2019s sown area","volume":"9","author":"Beurs","year":"2014","journal-title":"J. Land Use Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/isprs-annals-IV-3-45-2018","article-title":"Globally increased crop growth and cropping intensity from the long-term satellite-based observations","volume":"4","author":"Chen","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ding, M., Chen, Q., Xiao, X., Xin, L., Zhang, G., and Li, L. (2016). Variation in cropping intensity in northern China from 1982 to 2012 based on GIMMS-NDVI data. Sustainability, 8.","DOI":"10.3390\/su8111123"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.scitotenv.2015.11.129","article-title":"Spatio-temporal analysis of agricultural land-use intensity across the Western Siberian grain belt","volume":"544","author":"Broll","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"74","DOI":"10.4081\/ija.2015.656","article-title":"Indicators of agricultural intensity and intensification: A review of the literature","volume":"10","author":"Marraccini","year":"2015","journal-title":"Ital. J. Agron."},{"key":"ref_26","first-page":"43","article-title":"Remote sensing extraction of crop planting structure oriented to agricultural regionalization","volume":"38","author":"Liu","year":"2017","journal-title":"Chin. J. Agric. Resour. Reg. Plan."},{"key":"ref_27","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 US Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"1002","article-title":"New research paradigm for global land cover mapping","volume":"20","author":"Gong","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_29","first-page":"551","article-title":"Concepts and KeyTechniques for 30 m Clobal Land Cover Mapping","volume":"43","author":"Chen","year":"2014","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_30","first-page":"97","article-title":"A new methodology to map double-cropping croplands based on continuous wavelet transform","volume":"26","author":"Qiu","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","first-page":"594","article-title":"Integrated physical Regionalization of China","volume":"18","author":"Huang","year":"1959","journal-title":"Chin. Sci. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/BF00994110","article-title":"A Bayesian method for the induction of probabilistic networks from data","volume":"9","author":"Cooper","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_33","unstructured":"Pollino, C., and Henderson, C. (2019, January 16). Bayesian networks: A guide for their application in natural resource management and policy. Available online: https:\/\/bit.ly\/2FCThZa."},{"key":"ref_34","unstructured":"Morgan, M.G., Henrion, M., and Small, M. (1992). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge University Press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6569","DOI":"10.1080\/01431161.2010.512934","article-title":"Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image","volume":"32","author":"Dlamini","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160902897858","article-title":"A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas","volume":"31","author":"Wardlow","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","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_38","first-page":"188","article-title":"Mapping crop phenology using NDVI time-series derived from HJ-1 A\/B data","volume":"34","author":"Pan","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","first-page":"1080","article-title":"Spatial and temporal variations of multiple cropping index in China based on SPOT-NDVI during 1999\u20132013","volume":"70","author":"Ding","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_40","first-page":"218","article-title":"Remote-sensing based monitoring of planting structure and growth condition of major crops in Northeast China","volume":"26","author":"Huang","year":"2010","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_41","first-page":"278","article-title":"Integrating crop phenophase information in large-area crop condition evaluation with remote sensing","volume":"29","author":"Meng","year":"2014","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_42","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\u2013EVI time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","first-page":"81","article-title":"Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data","volume":"12","author":"Lunetta","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.3390\/rs5073190","article-title":"Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method","volume":"5","author":"You","year":"2013","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/168\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:47Z","timestamp":1760185607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,17]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020168"],"URL":"https:\/\/doi.org\/10.3390\/rs11020168","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,17]]}}}