{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:33:48Z","timestamp":1774935228970,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T00:00:00Z","timestamp":1573603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771463, 41771469, 61672032"],"award-info":[{"award-number":["41771463, 41771469, 61672032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Provincial Major Science and Technology Projects","award":["18030701209"],"award-info":[{"award-number":["18030701209"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017\u20132018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries\u2013Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making.<\/jats:p>","DOI":"10.3390\/rs11222647","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T09:11:27Z","timestamp":1573636287000},"page":"2647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions"],"prefix":"10.3390","volume":"11","author":[{"given":"Dongyan","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]},{"given":"Shengmei","family":"Fang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]},{"given":"Bao","family":"She","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"},{"name":"School of Geodesy and Geomatics, Anhui University of Science and Technology, Huainan 232001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-6086","authenticated-orcid":false,"given":"Huihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"},{"name":"Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO 80526, USA"}]},{"given":"Ning","family":"Jin","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"},{"name":"Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong 030600, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0106-6709","authenticated-orcid":false,"given":"Haoming","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"}]},{"given":"Yuying","family":"Yang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]},{"given":"Yang","family":"Ding","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,13]]},"reference":[{"key":"ref_1","unstructured":"FAO Regional Office for Asia and the Pacific (2014). FAO statistical Yearbook 2014, Asia and the Pacific, Food and Agriculture, FAO Regional Office for Asia and the Pacific."},{"key":"ref_2","unstructured":"China Statistics Press (2018). China Rural Statistical Yearbook, China Statistics Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.eja.2005.06.001","article-title":"Quantifying production potentials of winter wheat in the North China Plain","volume":"24","author":"Wu","year":"2006","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, M., Zhang, X., Zeng, H., and Wu, B. (2016). Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sens., 8.","DOI":"10.3390\/rs8100824"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/S0034-4257(00)00165-6","article-title":"A comparison of parametric classification procedures of remotely sensed data applied on different landscape units","volume":"75","author":"Cottonec","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Pickens, A., Krylov, A., and Stehman, S.V. (2018). Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan. Remote Sens., 10.","DOI":"10.3390\/rs10040489"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, Y., Tao, B., Ren, W., Zourarakis, D.P., Masri, B.E., Sun, Z., and Tian, Q. (2019). An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images. Remote Sens., 11.","DOI":"10.3390\/rs11101191"},{"key":"ref_9","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 mode","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/S2095-3119(15)61304-1","article-title":"Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data","volume":"16","author":"Tao","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_11","first-page":"166","article-title":"Review of research advances in remote sensing monitoring of grain crop area","volume":"21","author":"Chen","year":"2005","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.eja.2014.05.009","article-title":"Evaluation of pixel-and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management","volume":"59","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.compag.2010.12.012","article-title":"Evaluating high resolution SPOT 5 satellite imagery for crop identification","volume":"75","author":"Yang","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden Markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2017.04.016","article-title":"Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species","volume":"129","author":"Shoko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2017.03.021","article-title":"Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index","volume":"195","author":"Korhonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2006.04.001","article-title":"The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM","volume":"103","author":"Foody","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8763","DOI":"10.1080\/01431161.2010.550647","article-title":"Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems","volume":"32","author":"Conrad","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multitemporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"160","article-title":"Investigation method for crop area using remote sensing sampling basedon GF-1 satellite data","volume":"31","author":"Liu","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_24","first-page":"587","article-title":"Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines","volume":"33","author":"Son","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_25","first-page":"18","article-title":"Anhui Winter Wheat Growing Remote Sensing Monitoring and Evaluation Methods Research","volume":"27","author":"Liu","year":"2011","journal-title":"Chin. Agric. Sci. Bull."},{"key":"ref_26","first-page":"86","article-title":"Analyzing and Zoning of the Eco-climate Suitability on Winter Wheat Varieties in Anhui Province","volume":"33","author":"Ma","year":"2012","journal-title":"Chin. J. Agrometeorol."},{"key":"ref_27","first-page":"227","article-title":"Application on remote sensing survey of abandoned farmlands in winter along the Huaihe River based on GF-1 image","volume":"35","author":"Shang","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_28","unstructured":"Statistics Bureau of Anhui Province (2018). Anhui Statistical Yearbook."},{"key":"ref_29","unstructured":"(2015, December 17). ESA Introducing Sentinel-2. Available online: http:\/\/www.esa.int\/Our_Activities\/Observing_the_Earth\/Copernicus\/Sentinel-2\/Introducing_Sentinel-2."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cooley, S.W., Smith, L.C., Stepan, L., and Mascaro, J. (2017). Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121306"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Houborg, R., and McCabe, M.F. (2016). High-Resolution NDVI from Planet\u2019s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture. Remote Sens., 8.","DOI":"10.3390\/rs8090768"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/01431160902882603","article-title":"Object-oriented method for urban vegetation mapping using IKONOS imagery","volume":"31","author":"Zhang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.rse.2005.08.011","article-title":"On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multitemporal classification","volume":"98","author":"McVicar","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2014.889863","article-title":"Random forest classification of crop type using multi-temporal terrasar-x dual-polarimetric data","volume":"5","author":"Sonobe","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_36","unstructured":"Rouse, J.W., Hass, R.H., Scheel, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plain with ERTS. Proceedings of the 3rd Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_40","first-page":"590","article-title":"A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)","volume":"9","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_41","first-page":"38","article-title":"An Effective Approach to Automatically Extract Urban Land-use from TM Imagery","volume":"7","author":"Zha","year":"2003","journal-title":"J. Remote Sens."},{"key":"ref_42","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote estimation of chlorophyll content in higher plant leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","first-page":"519","article-title":"Identification of main crops based on the univariate feature selection in Subei","volume":"21","author":"Wang","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a Random Forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","first-page":"1049","article-title":"Random Forest classification of multisource remote sensing and geographic data","volume":"2","author":"Gislason","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Symp."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1080\/01431161.2013.788261","article-title":"Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests","volume":"34","author":"Guan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Congalton, R., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data, CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4631","DOI":"10.1080\/01431161.2017.1325531","article-title":"Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble","volume":"38","author":"Fernandes","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","first-page":"313","article-title":"Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images","volume":"23","author":"Zhang","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.08.023","article-title":"Efficient corn and soybean mapping with temporal extend ability: A multi-year experiment using Landsat imagery","volume":"140","author":"Zhong","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_58","first-page":"44","article-title":"Discrimination of soybean areas through images EVI\/MODIS and analysis based on geo-object","volume":"18","author":"Frank","year":"2014","journal-title":"Rev. Bras. Eng. Agric. Ambient."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:34:00Z","timestamp":1760189640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,13]]},"references-count":58,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222647"],"URL":"https:\/\/doi.org\/10.3390\/rs11222647","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,13]]}}}