{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T23:57:51Z","timestamp":1771631871720,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["PID2020-115998RB-C22"],"award-info":[{"award-number":["PID2020-115998RB-C22"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["101060529"],"award-info":[{"award-number":["101060529"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU HORIZON-CL6-2021 GOVERNANCE-01 CALL Project","award":["PID2020-115998RB-C22"],"award-info":[{"award-number":["PID2020-115998RB-C22"]}]},{"name":"EU HORIZON-CL6-2021 GOVERNANCE-01 CALL Project","award":["101060529"],"award-info":[{"award-number":["101060529"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In arid and semi-arid regions, irrigation is crucial to mitigate water stress and yield loss. However, the overexploitation of water resources by the agricultural sector together with the climate change effects can lead to water scarcity. Effective regional water management depends on estimating irrigation demand using maps of irrigable areas or national and regional statistics of irrigated areas. These statistical data are not always of reliable quality because they generally do not reflect the updated spatial distribution of irrigated and rainfed fields. In this context, remote sensing provides reliable methods for gathering useful agricultural information from derived records. The combined use of optical and radar Earth Observation data enhances the probability of detecting irrigation events, which can improve the accuracy of irrigation mapping. Hence, we aimed to utilize Sentinel-1 (VV and VH) and Sentinel-2 (NDVI) data to classify irrigated fruit trees and rainfed ones in a study area located in the Castilla La-Mancha region in Spain. To obtain these time-series data from Sentinel-1 and Sentinel-2, which constitute the input data for the classification algorithms, a tool has been developed for automating the download from the Sentinel Hub. This tool downloads products organized by tiles for the region of interest and for the entire required time-series, ensuring the spatial repeatability of each pixel across all products and dates. The classification of irrigated plots was carried out by SVM Support Vector Machine. The employed methodology displayed promising results, with an overall accuracy of 88.4%, indicating the methodology\u2019s ability to detect irrigation over orchards that were declared as non-irrigated. These results were evaluated by applying the change detection method of the \u03c3p0 backscattering coefficient at plot scale.<\/jats:p>","DOI":"10.3390\/rs16030458","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T06:54:12Z","timestamp":1706165652000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Irrigation Detection Using Sentinel-1 and Sentinel-2 Time Series on Fruit Tree Orchards"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4294-1015","authenticated-orcid":false,"given":"Amal","family":"Chakhar","sequence":"first","affiliation":[{"name":"Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9874-5243","authenticated-orcid":false,"given":"David","family":"Hern\u00e1ndez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-4421","authenticated-orcid":false,"given":"Roc\u00edo","family":"Ballesteros","sequence":"additional","affiliation":[{"name":"Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5940-6123","authenticated-orcid":false,"given":"Miguel A.","family":"Moreno","sequence":"additional","affiliation":[{"name":"Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"879","DOI":"10.13031\/2013.36701","article-title":"Optimization of Water Use Efficiency Under High Frequency Irrigation\u20142. System Simulation and Dynamic Programming","volume":"18","author":"Howell","year":"1974","journal-title":"Trans. ASAE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"660","DOI":"10.2134\/agronj1974.00021962006600050017x","article-title":"Model or Predicting Plant Yield as Influenced by Water Use","volume":"66","author":"Hanks","year":"1974","journal-title":"Agron. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2298\/HEL0951019P","article-title":"Effect of Water Stress on Yield and Evapotranspiration of Sunwlower","volume":"32","year":"2009","journal-title":"Helia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121154","DOI":"10.1016\/j.jclepro.2020.121154","article-title":"Historical Assessment and Future Sustainability Challenges of Egyptian Water Resources Management","volume":"263","author":"Luo","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tzanakakis, V.A., Angelakis, A.N., Paranychianakis, N.V., Dialynas, Y.G., and Tchobanoglous, G. (2022). Challenges and Opportunities for Sustainable Management of Water Resources in the Island of Crete, Greece. Water, 14.","DOI":"10.3390\/w14071024"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Calicioglu, O., Flammini, A., Bracco, S., Bell\u00f9, L., and Sims, R. (2019). The Future Challenges of Food and Agriculture: An Integrated Analysis of Trends and Solutions. Sustainability, 11.","DOI":"10.3390\/su11010222"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.jhydrol.2007.11.023","article-title":"Spatial and Temporal Variability of Precipitation Maxima during 1960\u20132005 in the Yangtze River Basin and Possible Association with Large-Scale Circulation","volume":"353","author":"Zhang","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s00477-009-0324-0","article-title":"Climate Changes and Their Impacts on Water Resources in the Arid Regions: A Case Study of the Tarim River Basin, China","volume":"24","author":"Zhang","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jamali, M., Gohari, A., Motamedi, A., and Haghighi, A.T. (2022). Spatiotemporal Changes in Air Temperature and Precipitation Extremes over Iran. Water, 14.","DOI":"10.3390\/w14213465"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1016\/j.earscirev.2020.103348","article-title":"Challenges for Drought Assessment in the Mediterranean Region under Future Climate Scenarios","volume":"210","author":"Tramblay","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7791","DOI":"10.1029\/2018WR022792","article-title":"The Effect of Global Warming on Future Water Availability: CMIP5 Synthesis","volume":"54","author":"Ferguson","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.3390\/w6123934","article-title":"Sustainable Water Management in Urban, Agricultural, and Natural Systems","volume":"6","author":"Russo","year":"2014","journal-title":"Water"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.agsy.2019.01.005","article-title":"Scalable Pixel-Based Crop Classification Combining Sentinel-2 and Landsat-8 Data Time Series: Case Study of the Duero River Basin","volume":"171","author":"Piedelobo","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A Review of Algorithms and Challenges from Remote Sensing Perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.14358\/PERS.75.12.1383","article-title":"Influence of Resolution in Irrigated Area Mapping and Area Estimation","volume":"75","author":"Velpuri","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111400","DOI":"10.1016\/j.rse.2019.111400","article-title":"Mapping Three Decades of Annual Irrigation across the US High Plains Aquifer Using Landsat and Google Earth Engine","volume":"233","author":"Deines","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.isprsjprs.2019.07.005","article-title":"Mapping Irrigated Cropland Extent across the Conterminous United States at 30 m Resolution Using a Semi-Automatic Training Approach on Google Earth Engine","volume":"155","author":"Xie","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"100590","article-title":"Mapping and Quantifying Agricultural Irrigation in Heterogeneous Landscapes Using Google Earth Engine","volume":"23","author":"Zurqani","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"126129","DOI":"10.1016\/j.jhydrol.2021.126129","article-title":"Detecting and Mapping Irrigated Areas in a Mediterranean Environment by Using Remote Sensing Soil Moisture and a Land Surface Model","volume":"596","author":"Dari","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11860","DOI":"10.1002\/2017GL075733","article-title":"Irrigation Signals Detected From SMAP Soil Moisture Retrievals","volume":"44","author":"Lawston","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, C., Wang, G., Zhu, J., Tang, Y., Shen, P., and Zhu, Z. (2018). A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin. Sensors, 18.","DOI":"10.3390\/s18093139"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Elwan, E., Page, M.L., Jarlan, L., Baghdadi, N., Brocca, L., Modanesi, S., Dari, J., Segui, P.Q., and Zribi, M. (2022). Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data. Water, 14.","DOI":"10.3390\/w14050804"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Hajj, M.E., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil Moisture and Irrigation Mapping in a Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-697-2020","article-title":"Mapping Irrigated Areas Using Random Forest Based on GF-1 Multi-Spectral Data","volume":"43","author":"Lu","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Massari, C., Modanesi, S., Dari, J., Gruber, A., De Lannoy, G.J.M., Girotto, M., Quintana-Segu\u00ed, P., Le Page, M., Jarlan, L., and Zribi, M. (2021). A Review of Irrigation Information Retrievals from Space and Their Utility for Users. Remote Sens., 13.","DOI":"10.3390\/rs13204112"},{"key":"ref_29","unstructured":"(2023, March 27). JCRMO Memoria 2020\u20132021. Available online: http:\/\/jcrmo.org\/wp-content\/uploads\/2022\/05\/memoria-jcrmo-2020-2021.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Amin, G., Fayad, I., Zribi, M., Demarez, V., and Belhouchette, H. (2021). An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13132584"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Fayad, I., Zribi, M., Belhouchette, H., and Demarez, V. (2020). Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data. Remote Sens., 12.","DOI":"10.3390\/rs12091456"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing : Challenges and Opportunities","volume":"104","author":"Benediktsson","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote Sensing Big Data Computing : Challenges and Opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-L\u00f3pez, D., Piedelobo, L., Moreno, M.A., Chakhar, A., Ortega-Terol, D., and Gonz\u00e1lez-Aguilera, D. (2021). Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13081546"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Milcinski, G., and Kolaric, P. (2023, January 24\u201328). Sentinel Hub\u2014Federated on-Demand ARD Generation. Proceedings of the EGU General Assembly 2023, Vienna, Austria. EGU23-4160.","DOI":"10.5194\/egusphere-egu23-4160"},{"key":"ref_36","unstructured":"Climate Zones (2022, March 20). National Geographic Institute (NGI). Available online: https:\/\/www.ign.es\/espmap\/mapas_clima_bach\/pdf\/%0AClima_Mapa_1_2texto.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.biosystemseng.2022.10.015","article-title":"Yield Estimations in a Vineyard Based on High-Resolution Spatial Imagery Acquired by a UAV","volume":"224","author":"Ortega","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dyke, G., Rosenqvist, A., Killough, B., and Yuan, F. (2021, January 11\u201316). Intercomparison of Sentinel-1 Datasets From Google Earth Engine and the Sinergise Sentinel Hub Card4L Tool. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554039"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10002","DOI":"10.3390\/rs61010002","article-title":"Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data","volume":"6","author":"Baghdadi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TGRS.1981.350328","article-title":"Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part III\u2014Soil Tension","volume":"GE-19","author":"Dobson","year":"1981","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"(1986). Microwave Remote Sensing: Active and Passive. Volume 1\u2014Microwave Remote Sensing Fundamentals and Radiometry, Artech House."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1080\/01431160310001654392","article-title":"Semi-Empirical Calibration of the IEM Backscattering Model Using Radar Images and Moisture and Roughness Field Measurements","volume":"25","author":"Baghdadi","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TGRS.1983.350530","article-title":"Effects of Vegetation Cover on the Microwave Radiometric Sensitivity to Soil Moisture","volume":"GE-21","author":"Ulaby","year":"1983","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TGRS.1985.289497","article-title":"Microwave Dielectric Behavior of Wet Soil-Part I: Empirical Models","volume":"GE-23","author":"Hallikainen","year":"1985","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/36.134086","article-title":"An Empirical Model and an Inversion Technique for Radar Scattering from Bare Soil Surfaces","volume":"30","author":"Oh","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108124","DOI":"10.1016\/j.agee.2022.108124","article-title":"Assessing Almond Response to Irrigation and Soil Management Practices Using Vegetation Indexes Time-Series and Plant Water Status Measurements","volume":"339","author":"Intrigliolo","year":"2022","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chakhar, A., Hern\u00e1ndez-L\u00f3pez, D., Zitouna-Chebbi, R., Mahjoub, I., Ballesteros, R., and Moreno, M.A. (2022). Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia. Remote Sens., 14.","DOI":"10.3390\/rs14195013"},{"key":"ref_48","first-page":"102388","article-title":"Investigating the Impact of Classification Features and Classifiers on Crop Mapping Performance in Heterogeneous Agricultural Landscapes","volume":"102","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","first-page":"100599","article-title":"Performance Evaluation of Machine Learning Algorithms Using Optical and Microwave Data for LULC Classification","volume":"23","author":"Chachondhia","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:50Z","timestamp":1760104130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,24]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030458"],"URL":"https:\/\/doi.org\/10.3390\/rs16030458","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,24]]}}}