{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T19:18:37Z","timestamp":1773256717096,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,8]],"date-time":"2019-02-08T00:00:00Z","timestamp":1549584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Brazilian Research Council CNPq (Conselho Nacional do Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico \u2013 grant n\u00b0 454292\/2014-7","award":["454292\/2014-7"],"award-info":[{"award-number":["454292\/2014-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.<\/jats:p>","DOI":"10.3390\/rs11030334","type":"journal-article","created":{"date-parts":[[2019,2,11]],"date-time":"2019-02-11T03:26:01Z","timestamp":1549855561000},"page":"334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region"],"prefix":"10.3390","volume":"11","author":[{"given":"Cec\u00edlia","family":"Lira Melo de Oliveira Santos","sequence":"first","affiliation":[{"name":"School of Agricultural Engineering, FEAGRI, University of Campinas, UNICAMP, Campinas 13083-875, Sao Paulo, Brazil"}]},{"given":"Rubens","family":"Augusto Camargo Lamparelli","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center on Energy Planning, NIPE, University of Campinas, UNICAMP, Campinas 13083-896, Sao Paulo, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5017-8320","authenticated-orcid":false,"given":"Gleyce","family":"Kelly Dantas Ara\u00fajo Figueiredo","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, FEAGRI, University of Campinas, UNICAMP, Campinas 13083-875, Sao Paulo, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9710-5364","authenticated-orcid":false,"given":"St\u00e9phane","family":"Dupuy","sequence":"additional","affiliation":[{"name":"CIRAD, UMR TETIS, F-34398 Montpellier, France"}]},{"given":"Julie","family":"Boury","sequence":"additional","affiliation":[{"name":"Paris Institute of Technology for Life, Food and Environmental Sciences, AgroParisTech, 75231 Paris, France"}]},{"given":"Ana Cl\u00e1udia dos Santos","family":"Luciano","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, FEAGRI, University of Campinas, UNICAMP, Campinas 13083-875, Sao Paulo, Brazil"},{"name":"Brazilian Bioethanol Science and Technology Laboratory, CTBE, Brazilian Center for Research in Energy and Materials, CNPEM, Campinas 13083-970, Sao Paulo, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9772-263X","authenticated-orcid":false,"given":"Ricardo da Silva","family":"Torres","sequence":"additional","affiliation":[{"name":"Institute of Computing, University of Campinas, UNICAMP, Campinas 13083-852, Sao Paulo, Brazil"}]},{"given":"Guerric","family":"le Maire","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center on Energy Planning, NIPE, University of Campinas, UNICAMP, Campinas 13083-896, Sao Paulo, Brazil"},{"name":"Brazilian Bioethanol Science and Technology Laboratory, CTBE, Brazilian Center for Research in Energy and Materials, CNPEM, Campinas 13083-970, Sao Paulo, Brazil"},{"name":"CIRAD, UMR Eco&amp;Sols, Campinas 13083-896, Brazil"},{"name":"Eco&amp;Sols, University of Montpellier, CIRAD, INRA, IRD, Montpellier SupAgro, 34000 Montpellier, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a cultivated planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18371","DOI":"10.1073\/pnas.1004659107","article-title":"Forecasting potential global environmental costs of livestock production 2000\u20132050","volume":"107","author":"Pelletier","year":"2010","journal-title":"PNAS"},{"key":"ref_3","unstructured":"McIntype, B.D., Herren, H.R., Wakhungu, J., and Watson, R.T. (2009). Agriculture at a Crossroads\u2014Global Report, Island Press."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, F., Wang, Q., Dong, J., and Xu, Q. (2018). Spectral and spatial classification of hyperspectral images based on random multi-graphs. Remote Sens., 10.","DOI":"10.3390\/rs10081271"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Torbick, N., Huang, X., Ziniti, B., Johnson, D., Masek, J., and Reba, M. (2018). Fusion of moderate resolution earth observations for operational crop type mapping. Remote Sens., 10.","DOI":"10.3390\/rs10071058"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/isprsarchives-XL-7-W3-39-2015","article-title":"Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine","volume":"40","author":"Kolotii","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2014.10.009","article-title":"Cloud cover throughout the agricultural growing season\u2014Impacts on passive optical earth observations","volume":"156","author":"Whitcraft","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1111\/gcb.12838","article-title":"Mapping global cropland and field size","volume":"21","author":"Fritz","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","unstructured":"Formaggio, A.R., and Sanches, I.D. (2017). Sensoriamento Remoto em Agricultura, Oficina de Textos. [1st ed.]."},{"key":"ref_10","unstructured":"FAO\u2014Food and Agriculture Organization of the United Nations (2015). FAOSTAT Statistical Database 2015, FAO."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/LGRS.2017.2789120","article-title":"Campo Verde database: Seeking to improve agricultural remote sensing of tropical areas","volume":"15","author":"Feitosa","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","unstructured":"(2018, October 25). IBGE\u2014Instituto Brasileiro de Geografia e Estat\u00edstica- Censo Agropecu\u00e1rio 2017- Produ\u00e7\u00e3o Agr\u00edcola Municipal, Available online: https:\/\/sidra.ibge.gov.br\/pesquisa\/pam\/tabelas."},{"key":"ref_13","first-page":"22","article-title":"Efficiency assessment of using satellite data for crop area estimation in Ukraine","volume":"29","author":"Gallego","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2017.04.014","article-title":"Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.rse.2014.05.015","article-title":"Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil","volume":"152","author":"Dupuy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13208","DOI":"10.3390\/rs71013208","article-title":"An automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series","volume":"7","author":"Matton","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lebourgeois, V., Dupuy, S., Vintrou, \u00c9., Ameline, M., Butler, S., and B\u00e9gu\u00e9, A. (2017). A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (simulated Sentinel-2 time series, VHRS and DEM). Remote Sens., 9.","DOI":"10.3390\/rs9030259"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Geographic Object- Based Image Analysis (GEOBIA): A new name for a new discipline. Object-Based Image Analysis- Spatial Concepts for Knowledge- Driven Remote Sensing Applications, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1080\/01431161.2013.873151","article-title":"Combining per-pixel and object-based classifications for mapping land cover over large areas","volume":"35","author":"Costa","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2018.06.017","article-title":"Generalized space-time classifiers for monitoring sugarcane areas in Brazil","volume":"215","author":"Picoli","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"2920","article-title":"Filling gaps in vegetation index measurements for crop growth monitoring","volume":"6","author":"Huang","year":"2011","journal-title":"Afr. J. Agric. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.3390\/rs70201461","article-title":"A framework for defining spatially explicit earth observation requirements for a global agricultural monitoring initiative (GEOGLAM)","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2018.05.005","article-title":"Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series","volume":"213","author":"Dara","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compag.2015.05.001","article-title":"Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data","volume":"115","author":"Tatsumi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved early crop type identification by joint use of high temporal resolution sar and optical image time series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Eberhardt, I.D.R., Schultz, B., Rizzi, R., Sanches, I.D.A., Formaggio, A.R., Atzberger, C., Mello, M.P., Immitzer, M., Trabaquini, K., and Foschiera, W. (2016). Cloud cover assessment for operational crop monitoring systems in tropical areas. Remote Sens., 8.","DOI":"10.3390\/rs8030219"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS\u2013Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the landsat and MODIS surface reflectance: Predicting daily landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1615\/JAutomatInfScien.v46.i12.30","article-title":"Reconstruction of missing data in time-series of optical satellite images using self-organizing Kohonen maps","volume":"46","author":"Skakun","year":"2014","journal-title":"J. Autom. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho, D., Minh, T., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.3390\/rs10081217"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.04.004","article-title":"Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data","volume":"93","author":"Jia","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/S0034-4257(01)00343-1","article-title":"Season long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full polarization backscatter signatures over paddy rice field and their relationship with biological variables_20.pdf","volume":"81","author":"Inoue","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSTARS.2011.2106198","article-title":"Crop classification using short-revisit multitemporal SAR data","volume":"4","author":"Skriver","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","first-page":"62","article-title":"Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata","volume":"51","author":"Waldhoff","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of classification algorithms and training sample sizes in urban land classification with landsat thematic mapper imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Berhane, T., Lane, C., Wu, Q., Autrey, B., Anenkhonov, O., Chepinoga, V., and Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sens., 10.","DOI":"10.3390\/rs10040580"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.rse.2009.01.010","article-title":"Land cover mapping of large areas using chain classification of neighboring Landsat satellite images","volume":"113","author":"Knorn","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","unstructured":"Li, J., and Yang, X. (2015). Support vector machines for land cover mapping from remote sensor imagery. Monitoring and Modeling of Global Changes: A Geomatics Perspective, Springer."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Noi, P.T., and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1002\/joc.4372","article-title":"Camargo, K\u00f6ppen and Thornthwaite climate classification systems in defining climatical regions of the state of S\u00e3o Paulo, Brazil","volume":"36","author":"Lucas","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_52","unstructured":"Camargo, A.D. (2019, February 08). Classifica\u00e7\u00e3o Clim\u00e1tica para Zoneamento de Aptid\u00e3o Agroclim\u00e1tica. Available online: https:\/\/scholar.google.co.uk\/scholar?hl=en&as_sdt=0%2C5&q=+Classifica%C3%A7%C3%A3o+clim%C3%A1tica+para+zoneamento+de+aptid%C3%A3o+agroclim%C3%A1tica&btnG=."},{"key":"ref_53","first-page":"141","article-title":"Nova classifica\u00e7\u00e3o clim\u00e1tica do Estado do Rio Grande do Sul. A new climatic classification for the State of Rio Grande do Sul, Brazil","volume":"8","author":"Maluf","year":"2000","journal-title":"Rev. Bras. Agrometeorol."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Schmidt, G., Jenkerson, C., Masek, J., Vermote, E., and Gao, F. (2013). Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description. Open File Rep. 2013\u20131057, 1\u201327.","DOI":"10.3133\/ofr20131057"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and De Jong, R. (2017). Barest pixel composite for agricultural areas using landsat time series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.rse.2012.04.011","article-title":"Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas","volume":"123","author":"Vieira","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1080\/01431160500296735","article-title":"Multi-temporal analysis of MODIS data to classify sugarcane crop","volume":"27","author":"Xavier","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"1","author":"Rouse","year":"1973","journal-title":"Third ERTS Symp. NASA"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.1109\/IGARSS.1997.609169","article-title":"The use of vegetation indices in forested regions: issues of linearity and saturation","volume":"Volume 4","author":"Huete","year":"1997","journal-title":"Geoscience and Remote Sensing, 1997. IGARSS\u201997. Remote Sensing-A Scientific Vision for Sustainable Development, 1997 IEEE International"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(95)00137-P","article-title":"Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data","volume":"54","author":"Hall","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1080\/014311697217369","article-title":"Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: The application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil","volume":"18","author":"Madeira","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"Missforest-non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Waldner, F., Bellemans, N., Hochman, Z., Newby, T., De Abelleyra, D., Santiago, R., Bartalev, S., Lavreniuk, M., Kussul, N., and Le Maire, G. (2019). Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. Int. J. Appl. Earth Obs. Geoinf., in press.","DOI":"10.1016\/j.jag.2019.01.002"},{"key":"ref_76","first-page":"1895","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Ergeb. Math. Ihrer Grenzgeb."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison: Evaluating the tatistical significance of differences in classificagtion accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1023\/A:1009752403260","article-title":"On comparing classifiers: Pitfalls to avoid and a recommended approach","volume":"1","author":"Salzberg","year":"1997","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Gr\u0105bczewski, K. (2014). Meta-Learning in Decision Tree Induction, Springer.","DOI":"10.1007\/978-3-319-00960-5"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0034-4257(99)00090-5","article-title":"Practical implications of design-based sampling inference for thematic map accuracy assessment","volume":"72","author":"Stehman","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_82","first-page":"431","article-title":"Using know map category marginal frequencies to improve estimates of thematic map accuracy","volume":"48","author":"Card","year":"1982","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., and Wu, W. (2017). In-season crop mapping with GF-1\/WFV data by combining object-based image analysis and random forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.isprsjprs.2018.08.007","article-title":"Big earth observation time series analysis for monitoring Brazilian agriculture","volume":"145","author":"Cristina","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.01.006","article-title":"Automated crop fi eld extraction from multi-temporal Web Enabled Landsat Data","volume":"144","author":"Yan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2014.10.014","article-title":"Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape","volume":"156","author":"Ru","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10661-009-0988-4","article-title":"Land cover mapping of the tropical savanna region in Brazil","volume":"116","author":"Sano","year":"2010","journal-title":"Environ. Monit. Assess."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.03.019","article-title":"Using spectrotemporal indices to improve the fruit-tree crop classification accuracy","volume":"128","author":"Liao","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_90","unstructured":"Luciano, A.C., dos, S., Picoli, M.C.A., Rocha, J.V., Duft, D., Lamparelli, R.A.C., Leal, M.R., and Le Maire, G. Regional estimations of sugarcane areas using Landsat time-series images and the random forest algorithm. J. Photogramm. Remote Sens., Under review."},{"key":"ref_91","unstructured":"ABRAF (2013). Anu\u00e1rio Estat\u00edstico ABRAF 2013\u2014Ano Base 2012, ABRAF."},{"key":"ref_92","first-page":"394","article-title":"Improving Local Per Level Hierarchical Classification","volume":"3","author":"Paes","year":"2012","journal-title":"J. Inf. Data Manag."},{"key":"ref_93","first-page":"711","article-title":"Land use classification from multitemporal landsat imagery using the yearly land cover dynamics (YLCD) method","volume":"13","author":"Julien","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.compag.2009.06.004","article-title":"Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery","volume":"68","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"14482","DOI":"10.3390\/rs71114482","article-title":"Self-guided segmentation and classification of multi-temporal Landsat 8 images for Crop type mapping in Southeastern Brazil","volume":"7","author":"Schultz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/36.481896","article-title":"Knowledge based land-cover classification using ERS-1\/JERS-1 SAR Composites","volume":"34","author":"Dobson","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/36.752214","article-title":"Experimental and model investigation on radar classification capability","volume":"37","author":"Ferrazzoli","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.3390\/rs6032372","article-title":"Multi-temporal polarimetric RADARSAT-2 for land cover monitoring in Northeastern Ontario, Canada","volume":"6","author":"Cable","year":"2014","journal-title":"Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_100","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:41Z","timestamp":1760185901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,8]]},"references-count":100,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030334"],"URL":"https:\/\/doi.org\/10.3390\/rs11030334","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,8]]}}}