{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:21:52Z","timestamp":1783610512316,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T00:00:00Z","timestamp":1533254400000},"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>The development and improvement of methods to map agricultural land cover are currently major challenges, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. The European Space Agency Sentinel-1 constellation, which recently became operational, is a satellite system providing global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution of about 20 m. These data are valuable, as they provide spatial information on agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France. The data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We improved this radar time series dataset by exploiting temporal filtering to reduce noise, while retaining as much as possible the fine structures present in the images. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure\/Accuracy greater than 86% and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of the Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96%. These results thus highlight that in the near future these RNN-based techniques will play an important role in the analysis of remote sensing time series.<\/jats:p>","DOI":"10.3390\/rs10081217","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T11:03:26Z","timestamp":1533294206000},"page":"1217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":195,"title":["Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4816-5579","authenticated-orcid":false,"given":"Emile","family":"Ndikumana","sequence":"first","affiliation":[{"name":"UMR TETIS, IRSTEA, University of Montpellier, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0116-1642","authenticated-orcid":false,"given":"Dinh","family":"Ho Tong Minh","sequence":"additional","affiliation":[{"name":"UMR TETIS, IRSTEA, University of Montpellier, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"UMR TETIS, IRSTEA, University of Montpellier, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominique","family":"Courault","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH, INRA, University of Avignon, 84914 Avignon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laure","family":"Hossard","sequence":"additional","affiliation":[{"name":"UMR 0951 INNOVATION, INRA, University of Montpellier, 34060 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.envsci.2012.10.002","article-title":"The potential to reduce the risk of diffuse pollution from agriculture while improving economic performance at farm level","volume":"25","author":"Buckley","year":"2013","journal-title":"Environ. Sci. Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_3","unstructured":"Polsot, A., Speedy, A., and Kueneman, E. (2004, January 27\u201329). Good Agricultural Practices\u2014A Working Concept. Proceedings of the FAO Internal Workshop on Good Agricultural Practices, Rome, Italy."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2 ESA Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/36.551933","article-title":"Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results","volume":"35","author":"Ribbes","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Mouret, J.C. (1988). Etude de l\u2019Agrosyst\u00e8me Rizicole en Camargue dans ses Relations avec le Milieu et le Systeme Cultural: Aspects Particuliers de la Fertilite. [Ph.D. Thesis, Universit\u00e9 des Sciences et Techniques du Languedoc]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.eja.2011.06.006","article-title":"On farm assessment of rice yield variability and productivity gaps between organic and conventional cropping systems under Mediterranean climate","volume":"35","author":"Delmotte","year":"2011","journal-title":"Eur. J. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.isprsjprs.2009.01.003","article-title":"Classifier ensembles for land cover mapping using multitemporal SAR imagery","volume":"64","author":"Waske","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","first-page":"4085","article-title":"Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/LGRS.2008.916070","article-title":"Semisupervised Image Classification with Laplacian Support Vector Machines","volume":"5","author":"Calpe","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1109\/TGRS.2010.2045764","article-title":"Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data","volume":"48","author":"Bovolo","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/JSTSP.2011.2139193","article-title":"A survey of active learning algorithms for supervised remote sensing image classification","volume":"5","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1109\/LGRS.2014.2368988","article-title":"Analysis of Multitemporal Classification Techniques for Forecasting Image Time Series","volume":"12","author":"Flamary","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/LGRS.2018.2794581","article-title":"Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1","volume":"15","author":"Ienco","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/LGRS.2017.2728698","article-title":"Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks","volume":"14","author":"Ienco","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","unstructured":"Hochreiter, S., and Schmidhuber, J. (1996, January 2\u20135). LSTM can Solve Hard Long Time Lag Problems. Proceedings of the NIPS, Denver, CO, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the EMNLP, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.jag.2017.01.001","article-title":"Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France","volume":"57","author":"Manfron","year":"2017","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TGRS.2011.2178247","article-title":"TOPS Interferometry With TerraSAR-X","volume":"50","author":"Scheiber","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/36.964973","article-title":"Filtering of multichannel SAR images","volume":"39","author":"Quegan","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Ho Tong Minh, D., Ngo, Y.-N., Baghdadi, N., and Maurel, P. (2016, January 9\u201313). TomoSAR platform: A new Irstea service as demand for SAR, Interferometry, Polarimetry and Tomography. Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ho Tong Minh, D., and Ngo, Y.-N. (2017, January 23\u201328). Tomosar platform supports for Sentinel-1 tops persistent scatterers interferometry. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127297"},{"key":"ref_29","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_30","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","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_33","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_34","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_35","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_36","doi-asserted-by":"crossref","first-page":"12160","DOI":"10.3390\/rs70912160","article-title":"Comparative analysis of MODIS time-series classification using support vector machines and methods based upon distance and similarity measures in the Brazilian Cerrado-Caatinga boundary","volume":"7","author":"Abade","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10462-007-9052-3","article-title":"Machine learning: A review of classification and combining techniques","volume":"26","author":"Kotsiantis","year":"2006","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1162\/NECO_a_00715","article-title":"Simultaneous Multichannel Signal Transfers via Chaos in a Recurrent Neural Network","volume":"27","author":"Soma","year":"2015","journal-title":"Neural Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1162\/tacl_a_00115","article-title":"Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies","volume":"4","author":"Linzen","year":"2016","journal-title":"TACL"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE TPAMI"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G.E. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","first-page":"27","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM TIST"},{"key":"ref_44","unstructured":"Chollet, F. (2015, May 10). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_45","unstructured":"Dauphin, Y.N., de Vries, H., Chung, J., and Bengio, Y. (arXiv, 2015). RMSProp and equilibrated adaptive learning rates for non-convex optimization, arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TSMCB.2008.2002909","article-title":"SVMs modeling for highly imbalanced classification","volume":"39","author":"Tang","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/j.patrec.2008.06.002","article-title":"A novel modular neural network for imbalanced classification problems","volume":"30","author":"Zhao","year":"2009","journal-title":"Pattern Recognit. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:16:21Z","timestamp":1760195781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,3]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081217"],"URL":"https:\/\/doi.org\/10.3390\/rs10081217","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,3]]}}}