{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:31:58Z","timestamp":1770971518074,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS\u2013NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001\u20132017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately \u22120.28 days\/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.<\/jats:p>","DOI":"10.3390\/rs11222668","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T11:24:32Z","timestamp":1573817072000},"page":"2668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Crop Group-Specific Pure Pixel Time Series for Europe"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6525-0455","authenticated-orcid":false,"given":"Christof J.","family":"Weissteiner","sequence":"first","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"given":"Ra\u00fal","family":"L\u00f3pez-Lozano","sequence":"additional","affiliation":[{"name":"French National Institute for Agricultural Research (INRA), UMR EMMAH, CEDEX 9, 84914 Avignon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6171-1525","authenticated-orcid":false,"given":"Giacinto","family":"Manfron","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6471-8404","authenticated-orcid":false,"given":"Gr\u00e9gory","family":"Duveiller","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"given":"Josh","family":"Hooker","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9103-7081","authenticated-orcid":false,"given":"Marijn","family":"van der Velde","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-6636","authenticated-orcid":false,"given":"Bettina","family":"Baruth","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.agsy.2018.05.001","article-title":"Use and relevance of European Union crop monitoring and yield forecasts","volume":"168","author":"Biavetti","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.agrformet.2015.02.021","article-title":"Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level","volume":"206","author":"Duveiller","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7959","DOI":"10.3390\/rs70607959","article-title":"Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps","volume":"7","author":"Waldner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isprsjprs.2008.07.006","article-title":"Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories","volume":"64","author":"McNairn","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","first-page":"121","article-title":"Effect of using crop specific masks on earth observation based crop yield forecasting across Canada","volume":"13","author":"Zhang","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.agsy.2018.05.010","article-title":"A comparison of global agricultural monitoring systems and current gaps","volume":"168","author":"Fritz","year":"2018","journal-title":"Agric. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1111\/geb.12243","article-title":"An analysis of methodological and spatial differences in global cropping systems models and maps: A comparative analysis of global cropping systems models","volume":"24","author":"Anderson","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1080\/01431161.2014.883106","article-title":"Obtaining crop-specific time profiles of NDVI: The use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V timeseries","volume":"35","author":"Atzberger","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.3390\/rs5031335","article-title":"Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.2134\/agronj2007.0170","article-title":"Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets","volume":"99","author":"Chang","year":"2007","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Durgun, \u00d6.Y., Gobin, A., Van De Kerchove, R., and Tychon, B. (2016). Crop Area Mapping Using 100-m Proba-V Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8070585"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.07.014","article-title":"Remotely sensed green area index for winter wheat crop monitoring: 10-Year assessment at regional scale over a fragmented landscape","volume":"166\u2013167","author":"Duveiller","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01431169208904046","article-title":"Linear mixture modelling applied to AVHRR data for crop area estimation","volume":"13","author":"Quarmby","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.14358\/PERS.72.1.55","article-title":"Estimation of Inter-Annual Crop Area Variation by the Application of Spectral Angle Mapping to Low Resolution Multitemporal NDVI Images","volume":"72","author":"Rembold","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jenvman.2012.12.019","article-title":"The harmonised data model for assessing Land Parcel Identification Systems compliance with requirements of direct aid and agri-environmental schemes of the CAP","volume":"118","author":"Sagris","year":"2013","journal-title":"J. Environ. Manag."},{"key":"ref_19","unstructured":"Bertaglia, M., Milenov, P., Angileri, V., and Devos, W. (2016). Cropland and Grassland Management Data Needs from Existing IACS Sources, Publications Office of the European Union."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"(2019, November 11). National Aeronautics and Space Administration (NASA): Moderate Resolution Imaging Spectroradiometer (MODIS), Available online: http:\/\/modis.gsfc.nasa.gov."},{"key":"ref_23","unstructured":"Vermote, E., and Wolfe, R. (2019, November 11). MOD09GQ MODIS\/Terra Surface Reflectance Daily L2G Global 250 m SIN Grid V006 (Data Set), Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/missions-and-measurements\/products\/MOD09GQ\/."},{"key":"ref_24","unstructured":"Cerrani, I., and L\u00f3pez Lozano, R. (2017). Algorithm for the Dissagregation of Crop Area Statistics in the MARS Crop Yield Forecasting System, European Commission, DG Joint Research Centre."},{"key":"ref_25","unstructured":"(2019, November 11). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/dhus\/."},{"key":"ref_26","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, NASA\/GSFC."},{"key":"ref_27","unstructured":"(2018, March 10). MCSNCyL Castile and Leon Crops and Natural Land Map (MCSNCyL). Available online: http:\/\/www.mcsncyl.itacyl.es\/en\/inicio."},{"key":"ref_28","unstructured":"Del Blanco Medina, V., and Nafr\u00eda Garc\u00eda, D.A. (2018, March 10). Mapa de cultivos y superficies naturales de Castilla y Le\u00f3n. Available online: https:\/\/web.archive.org\/web\/20161022230301\/http:\/\/www.congreso2015aet.com\/web\/descargas\/XVI_Congreso_AET_libro_actas_BAJA.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Paredes G\u00f3mez, V., Del Blanco Medina, V., Bengoa, J.L., and Nafr\u00eda Garc\u00eda, D.A. (2018, January 22\u201327). Accuracy assessment of a 122 classes land cover map based on Sentinel-2, Landsat 8, and Deimos-1 images and ancillary data. Proceedings of the Observing, Understanding and Forecasting the Dynamics of Our Planet, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519262"},{"key":"ref_30","unstructured":"EEA (2018, March 10). Corine Land Cover (CLC) 2012. Available online: https:\/\/land.copernicus.eu\/pan-european\/corine-land-cover\/clc-2012?tab=metadata."},{"key":"ref_31","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2008). Hole-Filled SRTM for the Globe Version 4, CGIAR."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.ecolind.2015.09.032","article-title":"A new view on EU agricultural landscapes: Quantifying patchiness to assess farmland heterogeneity","volume":"61","author":"Weissteiner","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"R Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_35","unstructured":"Vermote, E.F., Roger, J.C., and Ray, J.P. (2015, May 01). MODIS Surface Reflectance User\u2019s Guide. Available online: https:\/\/www.google.com.hk\/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=2ahUKEwjS29Wn397lAhUMyYsBHcEIDWwQFjABegQIBRAC&url=http%3A%2F%2Fmodis-sr.ltdri.org%2Fguide%2FMOD09_UserGuide_v1.4.pdf&usg=AOvVaw2vwPDJeHMQLn5blvGXA1VO."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"93","DOI":"10.5194\/asr-11-93-2014","article-title":"An overview of the phenological observation network and the phenological database of Germany\u2019s national meteorological service (Deutscher Wetterdienst)","volume":"11","author":"Kaspar","year":"2015","journal-title":"Adv. Sci. Res."},{"key":"ref_37","first-page":"190","article-title":"Estimating inter-annual variability in winter wheat sowing dates from satellite timeseries in Camargue, France","volume":"57","author":"Manfron","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"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","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.fcr.2019.03.015","article-title":"Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA","volume":"238","author":"Seo","year":"2019","journal-title":"Field Crop. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the Regression Coefficient Based on Kendall\u2019s Tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_41","unstructured":"Kendall, M.G. (1975). Rank Correlation Methods, Charles Griffin. [4th ed.]."},{"key":"ref_42","unstructured":"Fraley, C., Raftery, A.E., Murphy, T.B., and Scrucca, L. (2012). Mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation, Department of Statistics, University of Washington."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431169408954055","article-title":"A simplified method for the atmospheric of satellite measurements in the solar spectrum","volume":"15","author":"Rahman","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","first-page":"1","article-title":"Sentinel-2 MSI\u2013Level 2A Products Algorithm Theoretical Basis Document","volume":"46","author":"Richter","year":"2012","journal-title":"ESA SP"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1017\/S0013091500077853","article-title":"On a new method of graduation","volume":"41","author":"Whittaker","year":"1923","journal-title":"Proc. Edinb. Math. Soc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.scitotenv.2019.01.394","article-title":"Contrasting wheat phenological responses to climate change in global scale","volume":"665","author":"Ren","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10400","DOI":"10.3390\/rs70810400","article-title":"Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series","volume":"7","author":"Waldner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2015.06.001","article-title":"Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications","volume":"166","author":"Duveiller","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"SAPM (2019, August 23). Survey on Agricultural Production Methods. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php\/Survey_on_agricultural_production_methods."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.2134\/agronj2004.1139","article-title":"Monitoring Maize (Zea mays L.) Phenology with Remote Sensing","volume":"96","author":"Gitelson","year":"2004","journal-title":"Agron. J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Exp\u00f3sito, A., and Berbel, J. (2017). Agricultural Irrigation Water Use in a Closed Basin and the Impacts on Water Productivity: The Case of the Guadalquivir River Basin (Southern Spain). Water, 9.","DOI":"10.3390\/w9020136"},{"key":"ref_55","first-page":"36","article-title":"Tres d\u00e9cadas de pol\u00edtica de aguas en Andaluc\u00eda. An\u00e1lisis de procesos y perspectiva territorial","volume":"53","year":"2014","journal-title":"Cuadernos Geogr\u00e1ficos"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2668\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:34:36Z","timestamp":1760189676000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2668"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,15]]},"references-count":55,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222668"],"URL":"https:\/\/doi.org\/10.3390\/rs11222668","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,15]]}}}