{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:58:42Z","timestamp":1771959522427,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Minist\u00e8re de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","award":["grant 2016"],"award-info":[{"award-number":["grant 2016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribution of C\/L-Band frequency, dual\/quad polarization and the density of image time-series to winter land-use identification in an agricultural area of approximately 130 km\u00b2 located in northwestern France. First, SAR parameters were derived from RADARSAT-2, Sentinel-1 and Advanced Land Observing Satellite 2 (ALOS-2) time-series, and one quad-pol and six dual-pol datasets with different spatial resolutions and densities were calculated. Then, land use was classified using the Random Forest algorithm with each of these seven SAR datasets to determine the most suitable SAR configuration for identifying winter land-use. Results highlighted that (i) the C-Band (F1-score 0.70) outperformed the L-Band (F1-score 0.57), (ii) quad polarization (F1-score 0.69) outperformed dual polarization (F1-score 0.59) and (iii) a dense Sentinel-1 time-series (F1-score 0.70) outperformed RADARSAT-2 and ALOS-2 time-series (F1-score 0.69 and 0.29, respectively). In addition, Shannon Entropy and SPAN were the SAR parameters most important for discriminating winter land-use. Thus, the results of this study emphasize the interest of using Sentinel-1 time-series data for identifying winter land-use.<\/jats:p>","DOI":"10.3390\/s19245574","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T03:19:36Z","timestamp":1576811976000},"page":"5574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Polarimetric SAR Time-Series for Identification of Winter Land Use"],"prefix":"10.3390","volume":"19","author":[{"given":"Julien","family":"Denize","sequence":"first","affiliation":[{"name":"University of Rennes &amp; IETR UMR 6164, 35 000, Rennes, France"},{"name":"University of Rennes &amp; LETG UMR 6554, 35 000, Rennes, France"}]},{"given":"Laurence","family":"Hubert-Moy","sequence":"additional","affiliation":[{"name":"University of Rennes &amp; LETG UMR 6554, 35 000, Rennes, France"}]},{"given":"Eric","family":"Pottier","sequence":"additional","affiliation":[{"name":"University of Rennes &amp; IETR UMR 6164, 35 000, Rennes, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"ref_1","unstructured":"Fasona, M.J., and Omojola, A.S. (2005, January 21\u201323). Climate change, human security and communal clashes in Nigeria. Proceedings of the International Workshop on Human Security and Climate Change, Oslo, Norway."},{"key":"ref_2","first-page":"17","article-title":"Hi\u00e9rarchisation des facteurs structurant les dynamiques pluriannuelles des sols nus hivernaux. Application au bassin versant du Yar (Bretagne)","volume":"193","author":"Corgne","year":"2004","journal-title":"Norois Environ. Am\u00e9nage. Soci\u00e9t\u00e9"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E. (2019). Evaluation of using sentinel-1 and-2 time-series to identify winter land use in agricultural landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11010037"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.3390\/rs70505951","article-title":"Impact of sowing date on yield and water use efficiency of wheat analyzed through spatial modeling and FORMOSAT-2 images","volume":"7","author":"Duchemin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.5589\/m04-004","article-title":"An introduction to the RADARSAT-2 mission","volume":"30","author":"Morena","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Potin, P., Bargellini, P., Laur, H., Rosich, B., and Schmuck, S. (2012, January 22\u201327). Sentinel-1 mission operations concept. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351183"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S","article-title":"Satellite remote sensing of river inundation area, stage, and discharge: a review","volume":"11","author":"Smith","year":"1997","journal-title":"Hydrol. Process."},{"key":"ref_11","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1986, January 01). Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications, Available online: https:\/\/ntrs.nasa.gov\/search.jsp?R=19860041708."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2015.09.002","article-title":"Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C-and L-band radar data","volume":"170","author":"Hosseini","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"525","DOI":"10.5589\/m03-069","article-title":"The application of C-band polarimetric SAR for agriculture: a review","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_14","unstructured":"Mascolo, L. (2015). Polarimetric SAR for the Monitoring of Agricultural Crops. [Ph.D. Thesis, Universita\u2019degli Studi di Cagliari]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"H\u00fctt, C., Koppe, W., Miao, Y., and Bareth, G. (2016). Best accuracy land use\/land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens., 8.","DOI":"10.3390\/rs8080684"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1080\/01431161.2016.1204024","article-title":"Time series analysis of co-polarization phase difference (PPD) for winter field crops using polarimetric C-band SAR data","volume":"37","author":"Haldar","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TGRS.2011.2172994","article-title":"Crop classification by multitemporal C-and L-band single-and dual-polarization and fully polarimetric SAR","volume":"50","author":"Skriver","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2413","DOI":"10.1109\/36.789639","article-title":"Multitemporal C-and L-band polarimetric signatures of crops","volume":"37","author":"Skriver","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/07038992.2001.10854941","article-title":"Defining the sensitivity of multi-frequency and multi-polarized radar backscatter to post-harvest crop residue","volume":"27","author":"McNairn","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","unstructured":"Jiao, X., McNairn, H., Shang, J., and Liu, J. (2010, January 5\u20137). The sensitivity of multi-frequency (X, C and L-band) radar backscatter signatures to bio-physical variables (LAI) over corn and soybean fields. Proceedings of the ISPRS TC VII Symposium\u2014100 Years ISPRS, Vienna, Austria."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nurtyawan, R., Saepuloh, A., Budiharto, A., and Wikantika, K. (2016, January 19\u201320). Modeling Surface Roughness to Estimate Surface Moisture Using Radarsat-2 Quad Polarimetric SAR Data. Proceedings of the Journal of Physics: Conference Series, Bandung, Indonesia.","DOI":"10.1088\/1742-6596\/739\/1\/012105"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/07038992.1996.10855179","article-title":"Radar backscatter and crop residues","volume":"22","author":"Smith","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TGRS.1983.350472","article-title":"Radar backscattering properties of corn and soybeans at frequencies of 1.6, 4.75, and 13.3 Ghz","volume":"3","author":"Paris","year":"1983","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1109\/36.964970","article-title":"Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"757","DOI":"10.5194\/isprs-archives-XLI-B7-757-2016","article-title":"Land cover mapping using sentinel-1 SAR data","volume":"41","author":"Abdikan","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dimov, D., L\u00f6w, F., Ibrakhimov, M., Stulina, G., and Conrad, C. (2017, January 23\u201328). SAR and optical time series for crop classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127076"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rse.2017.06.022","article-title":"A new method for crop classification combining time series of radar images and crop phenology information","volume":"198","author":"Bargiel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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":"Minh","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","unstructured":"(2019, May 24). ZA Armorique. Available online: https:\/\/osur.univ-rennes1.fr\/za-armorique\/."},{"key":"ref_30","unstructured":"(2019, May 24). Nitrates\u2014Water Pollution\u2014Environment\u2014European Commission. Available online: http:\/\/ec.europa.eu\/environment\/water\/water-nitrates\/index_en.html."},{"key":"ref_31","unstructured":"(2019, September 10). Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_32","unstructured":"(2019, May 24). Kalideos. Available online: https:\/\/www.kalideos.fr\/drupal\/fr."},{"key":"ref_33","unstructured":"Pottier, E., Ferro-Famil, L., Fitrzyk, M., and Desnos, Y.-L. (2018, January 4\u20137). PolSARpro-BIO: The new Scientific Toolbox for ESA & third party fully Polarimetric SAR Missions. Proceedings of the 12th European Conference on Synthetic Aperture Radar, EUSAR 2018, Aachen, Germany."},{"key":"ref_34","unstructured":"(2017, November 20). Radarsat 1 Products Image Resolution Geomatics. Available online: https:\/\/www.scribd.com\/document\/73559622\/Radarsat-1-Products."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2008.2002881","article-title":"Improved sigma filter for speckle filtering of SAR imagery","volume":"47","author":"Lee","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/LGRS.2011.2174772","article-title":"Radar vegetation index for estimating the vegetation water content of rice and soybean","volume":"9","author":"Kim","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"517","DOI":"10.5589\/m03-068","article-title":"Applying polarimetric radar imagery for mapping the productivity of wheat crops","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1103\/PhysRevE.48.1752","article-title":"Noise reduction in chaotic time-series data: A survey of common methods","volume":"48","author":"Kostelich","year":"1993","journal-title":"Phys. Rev. E"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","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_45","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_46","unstructured":"(2019, September 10). R: The R Project for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/17538947.2016.1216615","article-title":"Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements","volume":"10","author":"Yusoff","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"McNairn, H., Shang, J., Champagne, C., and Jiao, X. (2009, January 12\u201317). TerraSAR-X and RADARSAT-2 for crop classification and acreage estimation. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5418243"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1109\/TGRS.2012.2208649","article-title":"Multiyear crop monitoring using polarimetric RADARSAT-2 data","volume":"51","author":"Liu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2528\/PIERB11071106","article-title":"Assessment of L-band SAR data at different polarization combinations for crop and other landuse classification","volume":"36","author":"Haldar","year":"2012","journal-title":"Prog. Electromagn. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2005.03.010","article-title":"Efficiency of crop identification based on optical and SAR image time series","volume":"96","author":"Blaes","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1016\/j.rse.2009.04.005","article-title":"Potential of SAR sensors TerraSAR-X, ASAR\/ENVISAT and PALSAR\/ALOS for monitoring sugarcane crops on Reunion Island","volume":"113","author":"Baghdadi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","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","volume":"81","author":"Inoue","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1109\/TGRS.2012.2189012","article-title":"Impact of reducing polarimetric SAR input on the uncertainty of crop classifications based on the random forests algorithm","volume":"50","author":"Loosvelt","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1051\/kmae\/2013068","article-title":"Monitoring restored riparian vegetation: How can recent developments in remote sensing sciences help?","volume":"410","author":"Dufour","year":"2013","journal-title":"Knowl. Manag. Aquat. Ecosyst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"109","DOI":"10.14358\/PERS.81.2.109","article-title":"Fully polarimetric synthetic aperture radar (SAR) processing for crop type identification","volume":"81","author":"Hong","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4721","DOI":"10.3390\/s110504721","article-title":"Crop classification by forward neural network with adaptive chaotic particle swarm optimization","volume":"11","author":"Zhang","year":"2011","journal-title":"Sensors"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5589\/m13-003","article-title":"Sensitivity of C-band SAR polarimetric variables to unvegetated agricultural fields","volume":"39","author":"Adams","year":"2013","journal-title":"Can. J. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5574\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:43:01Z","timestamp":1760190181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,17]]},"references-count":64,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245574"],"URL":"https:\/\/doi.org\/10.3390\/s19245574","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,17]]}}}