{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T11:52:43Z","timestamp":1781092363327,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,18]],"date-time":"2018-09-18T00:00:00Z","timestamp":1537228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["DI-15-08105"],"award-info":[{"award-number":["DI-15-08105"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ag\u00e8ncia de Gesti\u00f3 d\u2019Ajuts Universitaris i de Recerca","award":["DI-2016-078"],"award-info":[{"award-number":["DI-2016-078"]}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["645642"],"award-info":[{"award-number":["645642"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical\u2013vertical) and VH (vertical\u2013horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.<\/jats:p>","DOI":"10.3390\/rs10091495","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T10:50:31Z","timestamp":1537354231000},"page":"1495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Irrigation Mapping Using Sentinel-1 Time Series at Field Scale"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9291-6240","authenticated-orcid":false,"given":"Qi","family":"Gao","sequence":"first","affiliation":[{"name":"isardSAT, Parc Tecnol\u00f2gic Barcelona Activa, Carrer de Marie Curie, 8, 08042 Barcelona, Catalunya, Spain"},{"name":"CESBIO (CNRS\/CNES\/UPS\/IRD), CEDEX 9, 31401 Toulouse, France"},{"name":"Observatori de l\u2019Ebre (OE), Ramon Llull University, 43520 Roquetes, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"CESBIO (CNRS\/CNES\/UPS\/IRD), CEDEX 9, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7780-7334","authenticated-orcid":false,"given":"Maria Jose","family":"Escorihuela","sequence":"additional","affiliation":[{"name":"isardSAT, Parc Tecnol\u00f2gic Barcelona Activa, Carrer de Marie Curie, 8, 08042 Barcelona, Catalunya, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"IRSTEA, University of Montpellier, UMR TETIS, CEDEX 5, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-9671","authenticated-orcid":false,"given":"Pere Quintana","family":"Segui","sequence":"additional","affiliation":[{"name":"Observatori de l\u2019Ebre (OE), Ramon Llull University, 43520 Roquetes, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1126\/science.1089967","article-title":"Global freshwater resources: Soft-path solutions for the 21st century","volume":"302","author":"Gleick","year":"2003","journal-title":"Science"},{"key":"ref_2","unstructured":"Rosegrant, M.W., Meijer, S., and Cline, S.A. (2002). International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description, IFPRI."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/rs2092274","article-title":"Remote Sensing of Irrigated Agriculture: Opportunities and Challenges","volume":"2","author":"Ozdogan","year":"2010","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4697","DOI":"10.1007\/s11269-013-0438-5","article-title":"Assessment of Equity and Adequacy of Water Delivery in Irrigation Systems Using Remote Sensing-Based Indicators in Semi-Arid Region, Morocco","volume":"27","author":"Kharrou","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_5","unstructured":"Stefan, V. (2016). Mixed Modeling and Multi-Resolution Remote Sensing of Soil Evaporation. [Ph.D. Thesis, Universit\u00e9 Toulouse 3 Paul Sabatier (UT3 Paul Sabatier)]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"160118","DOI":"10.1038\/sdata.2016.118","article-title":"Data descriptor: Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015","volume":"3","author":"Ambika","year":"2016","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cai, X., Magidi, J., Nhamo, L., and Koppen, B. (2017). Mapping Irrigated Areas in the Limpopo Province, South Africa, IWMI. IWMI Working Paper 172.","DOI":"10.5337\/2017.205"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2004.12.018","article-title":"Ganges and Indus river basin land use\/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data","volume":"95","author":"Thenkabail","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"1177","article-title":"Irrigated crop area estimation using Landsat TM imagery in La Mancha, Spain","volume":"67","author":"Beltran","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1080\/01431160600851801","article-title":"Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India","volume":"27","author":"Biggs","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2009.08.004","article-title":"Irrigated areas of India derived using MODIS 500 m time series for the years 2001\u20132003","volume":"65","author":"Dheeravath","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1080\/01431160500104335","article-title":"Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data","volume":"26","author":"Kamthonkiat","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Draeger, W.C. (1977, January 25\u201329). Monitoring Irrigated Land Acreage Using LANDSAT Imagery: An Application Example. Proceedings of the 11th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, USA.","DOI":"10.3133\/ofr76630"},{"key":"ref_14","first-page":"1493","article-title":"Satellite sensing of irrigation pattern in semiarid areas: An Indian study","volume":"47","author":"Thiruvengadachari","year":"1981","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","first-page":"587","article-title":"The Nebraska center-pivot inventory\u2014An example of operational satellite remote sensing on a long term basis","volume":"55","author":"Rundquist","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1080\/01431160600784192","article-title":"Crop and land cover classification in Iran using Landsat 7 imagery","volume":"27","author":"Akbari","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1016\/j.rse.2008.04.010","article-title":"A New Methodology to Map Irrigated Areas Using Multi-Temporal MODIS and Ancillary Data: An Application Example in the Continental US","volume":"112","author":"Ozdogan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1080\/01431160310001618077","article-title":"Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute","volume":"25","author":"Boken","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"816","DOI":"10.3390\/rs3040816","article-title":"Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data","volume":"3","author":"Gumma","year":"2011","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"Baghdadi, N., and Zribi, M. (2016). Land Surface Remote Sensing in Continental Hydrology, Elsevier."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"256","DOI":"10.3390\/s8010256","article-title":"Soil moisture profile effect on radar signal measurement","volume":"8","author":"Morvan","year":"2008","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.5194\/hess-12-1415-2008","article-title":"Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)","volume":"12","author":"Anguela","year":"2008","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"345","DOI":"10.5194\/hess-15-345-2011","article-title":"Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation","volume":"15","author":"Zribi","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2014.05.009","article-title":"A new soil roughness parameter for the modelling of radar backscattering over bare soil","volume":"152","author":"Zribi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1080\/2150704X.2013.842285","article-title":"Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin","volume":"4","author":"Fontanelli","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/S0034-4257(02)00069-X","article-title":"A new empirical model to retrieve soil moisture and roughness from C-band radar data","volume":"84","author":"Zribi","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3844","DOI":"10.1109\/TGRS.2012.2185934","article-title":"A potential use for the C-band polarimetric SAR parameters to characterize the soil surface over bare agriculture fields","volume":"50","author":"Baghdadi","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/TGRS.2003.813356","article-title":"Use of multiincidence angle RADARSAT-1 SAR data to incorporate the effect of surface roughness in soil moisture estimation","volume":"41","author":"Srivastava","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Sikdar, M., and Cumming, I. (2004, January 20\u201324). A modified empirical model for soil moisture estimation in vegetated areas using SAR data. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8128","DOI":"10.3390\/rs70608128","article-title":"Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Tropical Region","volume":"7","author":"Tomer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., and Baghdadi, N. (2017). Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1080\/014311699213172","article-title":"Rice field mapping and monitoring with RADARSAT data","volume":"20","author":"Ribbes","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/S0034-4257(00)00212-1","article-title":"Rice monitoring and production estimation using multitemporal RADARSAT","volume":"76","author":"Shao","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1109\/TGRS.2007.904582","article-title":"A Method for Soil Moisture Estimation in Western Africa Based on the ERS Scatterometer","volume":"46","author":"Zribi","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/01431160500214050","article-title":"Comparative evaluation of the sensitivity of multi-polarized multi-frequency SAR backscatter to plant density","volume":"27","author":"Patel","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4919","DOI":"10.3390\/rs5104919","article-title":"Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data","volume":"5","author":"Jacome","year":"2013","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2018.04.013","article-title":"Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil","volume":"211","author":"Amazirh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jhydrol.2017.10.048","article-title":"Retrieving topsoil moisture using RADARSAT-2 data, a novel approach applied at the east of The Netherlands","volume":"555","author":"Eweys","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2010.07.011","article-title":"Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data","volume":"115","author":"Gherboudj","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"Karjalainen, M., Kaartinen, H., Hyypp\u00e4, J., Laurila, H., and Kuittinen, R. (2004, January 12\u201323). The Use of ENVISAT Alternating Ploarization SAR Images in Agricultureal Monitoring in Compatison with RADARSAT-1 SAR Images. Proceedings of the ISPRS Congress, Istanbul, Turkey."},{"key":"ref_44","first-page":"1","article-title":"Comparative evaluation of the sensitivity of multi-polarized SAR and optical data for various land cover classes","volume":"4","author":"Chauhan","year":"2016","journal-title":"Int. J. Adv. Remote Sens. GIS Geogr."},{"key":"ref_45","unstructured":"(2018, May 01). INFORMACI\u00d3 DE LES DADES SIGPAC. Available online: https:\/\/analisi.transparenciacatalunya.cat\/api\/views\/w9bf-jejh\/files\/36948005-55ef-4003-826b-d01c17968ddf?download=true&filename=Dades_SIGPAC_2017.pdf."},{"key":"ref_46","unstructured":"(2018, May 01). El Departament d\u2019Agricultura, Ramaderia, Pesca i Alimentaci\u00f3 (DARP)\u2014Mapa Agricultura. Available online: http:\/\/sig.gencat.cat\/visors\/Agricultura.html."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2016.02.046","article-title":"Comparison of remote sensing and simulated soil moisture datasets in mediterranean landscapes","volume":"180","author":"Escorihuela","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1086\/166773","article-title":"The discrete correlation function\u2014A new method for analyzing unevenly sampled variability data","volume":"333","author":"Edelson","year":"1988","journal-title":"Astrophys. J."},{"key":"ref_49","unstructured":"Mandelbrot, B.B. (1977). Fractals, Form, Chance and Dimension, W. H. Freeman and Company."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.3390\/rs3122568","article-title":"Analysis of vegetation behavior in a north African semi-arid region, using SPOT-Vegetation NDVI data","volume":"3","author":"Amri","year":"2011","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4963","DOI":"10.1080\/01431160600676695","article-title":"Fractal analysis of remotely sensed images: A review of methods and applications","volume":"27","author":"Sun","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","unstructured":"Menenti, M., Azzali, S., Vries, A., Fuller, D., and Prince, S. (1993, January 25\u201328). Vegetation monitoring in southern Africa using temporal fourrier analysis of AVHRR\/NDVI observations. Proceedings of the International Symposium on Remote Sensing in Arid and Semi-arid Regions, Lanzhou, China."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0378-4371(99)00333-7","article-title":"Application of statistical physics to heartbeat diagnosis","volume":"274","author":"Havlin","year":"1999","journal-title":"Physica A"},{"key":"ref_54","first-page":"774","article-title":"Pattern recognition using generalized portrait method","volume":"24","author":"Vapnik","year":"1963","journal-title":"Autom. Remote Control"},{"key":"ref_55","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The Random Subspace Method for Constructing Decision Forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Comaniciu, D., and Meer, P. (1999, January 20\u201327). Mean shift analysis and applications. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790416"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1495\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:21:19Z","timestamp":1760196079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,18]]},"references-count":59,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["rs10091495"],"URL":"https:\/\/doi.org\/10.3390\/rs10091495","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,18]]}}}