{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:50:44Z","timestamp":1761630644626,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2013,3,14]],"date-time":"2013-03-14T00:00:00Z","timestamp":1363219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of  sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper\/Enhanced Thematic Mapper (TM\/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s.<\/jats:p>","DOI":"10.3390\/rs5031335","type":"journal-article","created":{"date-parts":[[2013,3,14]],"date-time":"2013-03-14T12:22:04Z","timestamp":1363263724000},"page":"1335-1354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"first","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences (BOKU), A-1190 Vienna, Austria"}]},{"given":"Felix","family":"Rembold","sequence":"additional","affiliation":[{"name":"MARS Unit, Institute for the Protection and Security of the Citizen, Joint Research Center of the European Commission, Via Enrico Fermi 2749, I-21027 Ispra (VA), Italy"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rembold, F., Atzberger, C., Savin, I., and Rojas, O. (2013). Using low resolution imagery for yield prediction and yield anomaly detection. Remote Sens., in press.","DOI":"10.3390\/rs5041704"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs5010019","article-title":"Harmonizing and combining existing land cover\/land use datasets for cropland area monitoring at the African","volume":"5","author":"Vancutsem","year":"2013","journal-title":"Remote Sens"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2004.08.002","article-title":"Cropland distributions from temporal unmixing of MODIS data","volume":"93","author":"Lobell","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_5","unstructured":"Annoni, A., and Perdigao, V. (1997). Technical and Methodological Guide for Updating CORINE Land Cover Data Base, European Commission. EUR 17288EN."},{"key":"ref_6","first-page":"189","article-title":"Misclassification bias in areal estimates","volume":"58","author":"Czaplevsky","year":"1992","journal-title":"Photogram. Eng. Remote Sensing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1080\/01431160310001619607","article-title":"Remote sensing and land cover area estimation","volume":"25","author":"Gallego","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3972","DOI":"10.3390\/rs4123972","article-title":"Reconstructing the spatio-temporal development of irrigated production systems in Uzbekistan using Landsat time series","volume":"4","author":"Edlinger","year":"2012","journal-title":"Remote Sens"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3143","DOI":"10.3390\/rs4103143","article-title":"Exploiting the classification performance of Support Vector Machines with multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data in areas of agreement and disagreement of existing land cover products","volume":"4","author":"Vuolo","year":"2012","journal-title":"Remote Sens"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"20867","DOI":"10.1029\/95JD01536","article-title":"Mapping the land-surface for global atmosphere-biosphere models\u2014Toward continuous distributions of vegetations functional properties","volume":"100","author":"Defries","year":"1995","journal-title":"J. Geophys. Res"},{"key":"ref_14","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_15","first-page":"453","article-title":"The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia","volume":"10","author":"Fritz","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00079-2","article-title":"Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/S0034-4257(97)00131-4","article-title":"Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments","volume":"63","author":"Maselli","year":"1998","journal-title":"Remote Sens. Environ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2004.05.017","article-title":"Crop condition and yield simulations using Landsat and MODIS","volume":"92","author":"Doraiswamy","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1080\/01431160310001657614","article-title":"Estimating inter-annual crop area variation using multi-resolution satellite sensor images","volume":"25","author":"Rembold","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_21","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":"Photogram. Eng. Remote Sensing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1080\/014311697218836","article-title":"Mapping sub-pixel proportional land cover with AVHRR imagery","volume":"8","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.compag.2008.07.009","article-title":"Quantifying sub-pixel signature of paddy rice field using an artificial neural network","volume":"65","author":"Karkee","year":"2009","journal-title":"Comput. Electron. Agric"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2003.06.002","article-title":"A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: Application in the Brazilian Amazon region","volume":"87","author":"Braswell","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_26","first-page":"486","article-title":"Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium","volume":"10","author":"Verbeiren","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bossyns, B., Eerens, H., and van Orshoven, J. (2007, January 18\u201320). Crop Area Assessment Using Sub-Pixel Classification with a Neural Network Trained for a Reference Year. Leuven, Belgium.","DOI":"10.1109\/MULTITEMP.2007.4293038"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Atzberger, C., and Rembold, F. (2009). Estimation of inter-annual winter crop area variation and spatial distribution with low resolution NDVI data by using neural networks trained on high resolution images. Proc. SPIE.","DOI":"10.1117\/12.830007"},{"key":"ref_29","unstructured":"Gonzales-Villalobos, A., and Wallace, A. (1998). FAO Statistical Development Series, FAO. Chapter 13."},{"key":"ref_30","unstructured":"AGRIT 2009. Cereali Autunno-Vernini Statistiche Agronomiche di Superficie, Resa e Produzione; Bollettino Giugno 2009; Sistema Informativo Nazionale per lo sviluppo in Agricoltura: Rome, Italy, 2009."},{"key":"ref_31","unstructured":"Consorzio, I.T.A. (1987). Telerilevamento in Agricoltora, Previsione delle Produzioni di Frumento in Tempo Reale e Sviluppi Tecnologici, Ministero dell\u2019Agricultura."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1080\/01431169408954228","article-title":"An atmospheric correction method for automatic retrieval of surface reflectances from TM images","volume":"15","author":"Gilabert","year":"1994","journal-title":"Int. J. Remote Sens"},{"key":"ref_33","unstructured":"Weiss, M., Baret, F., Eerens, H., and Swinnen, E. (October, January 27). FAPAR over Europe for the Past 29 Years: A Temporally Consistent Product Derived from AVHRR and VEGETATION Sensors. Valencia, Spain."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6519","DOI":"10.1080\/01431161.2010.496473","article-title":"Calibrations for AVHRR Channels 1 and 2: Review and path towards consensus","volume":"31","author":"Molling","year":"2010","journal-title":"Int. J. Remote Sens"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3689","DOI":"10.1080\/01431161003762405","article-title":"Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements","volume":"32","author":"Atzberger","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(00)00153-X","article-title":"Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analysis","volume":"75","author":"Maselli","year":"2001","journal-title":"Remote Sens. Environ"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1080\/014311697218845","article-title":"Non-linear mixture modeling without end-members using an artificical neural net","volume":"18","author":"Foody","year":"1997","journal-title":"Int. J. Remote Sens"},{"key":"ref_44","unstructured":"Demuth, H., and Beale, M. (2003). Neural Network Toolbox User\u2019s Guide, Version 4, The MathWorks Inc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"371","DOI":"10.5721\/EuJRS20124532","article-title":"Portability of neural nets modelling regional winter crop acreages using AVHRR time series","volume":"45","author":"Atzberger","year":"2012","journal-title":"Eur. J. Remote Sens"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.rse.2003.10.022","article-title":"Impacts of imagery temporal frequency on land-cover change detection monitoring","volume":"89","author":"Lunetta","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3364","DOI":"10.3390\/rs4113364","article-title":"How NDVI trends from AVHRR and SPOT VGT time series differ in agricultural areas: An Inner Mongolian case study","volume":"4","author":"Yin","year":"2012","journal-title":"Remote Sens"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Meroni, M., Atzberger, C., Vancutsem, C., Gobron, N., Baret, F., Lacaze, R., Eerens, H., and Leo, O. (2012). Evaluation of agreement between space remote sensing SPOT-VEGETATION fAPAR time series. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2012.2212447"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/3\/1335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:33Z","timestamp":1760219133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/3\/1335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3,14]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2013,3]]}},"alternative-id":["rs5031335"],"URL":"https:\/\/doi.org\/10.3390\/rs5031335","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2013,3,14]]}}}