{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:08:35Z","timestamp":1768262915568,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["730109"],"award-info":[{"award-number":["730109"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.<\/jats:p>","DOI":"10.3390\/rs12081275","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-2574","authenticated-orcid":false,"given":"Salvatore","family":"Falanga Bolognesi","sequence":"first","affiliation":[{"name":"Ariespace s.r.l., Centro Direzionale IS. A3, 80143 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0799-3490","authenticated-orcid":false,"given":"Edoardo","family":"Pasolli","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples Federico II, Via Universit\u00e0 100, 80055 Portici, Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5748-4224","authenticated-orcid":false,"given":"Oscar","family":"Belfiore","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples Federico II, Via Universit\u00e0 100, 80055 Portici, Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3797-850X","authenticated-orcid":false,"given":"Carlo","family":"De Michele","sequence":"additional","affiliation":[{"name":"Ariespace s.r.l., Centro Direzionale IS. A3, 80143 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-4668","authenticated-orcid":false,"given":"Guido","family":"D\u2019Urso","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Naples Federico II, Via Universit\u00e0 100, 80055 Portici, Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/PL00012590","article-title":"Global water assessment and potential contributions from Earth Systems Science","volume":"64","year":"2002","journal-title":"Aquat. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s00382-004-0402-4","article-title":"Direct human influence of irrigation on atmospheric water vapour and climate","volume":"22","author":"Boucher","year":"2004","journal-title":"Clim. Dyn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1623\/hysj.48.3.339.45278","article-title":"Global estimates of water withdrawals and availability under current and future \u201cbusiness-as-usual\u201d conditions","volume":"48","author":"Alcamo","year":"2003","journal-title":"Hydrol. Sci. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2014.08.016","article-title":"Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability","volume":"154","author":"McVicar","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2014.04.008","article-title":"Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","volume":"149","author":"Pervez","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3390\/w3010113","article-title":"Mapping irrigated areas using MODIS 250 meter time-series data: A study on Krishna river basin (India)","volume":"3","author":"Gumma","year":"2011","journal-title":"Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.agsy.2014.01.004","article-title":"Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture","volume":"127","author":"Brown","year":"2014","journal-title":"Agric. Syst."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Pun, M., Mutiibwa, D., and Li, R. (2017). Land Use Classification: A surface energy balance and vegetation index application to map and monitor irrigated lands. Remote Sens., 9.","DOI":"10.3390\/rs9121256"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"Claverie, M., Masek, J.G., Ju, J., and Dungan, J.L. (2017). Harmonized Landsat-8 Sentinel-2 (HLS) Product User\u2019s Guide."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z., Gallant, A.L., Rundquist, B., and Burke, M. (2019). Monitoring landscape dynamics in Central U.S. grasslands with Harmonized Landsat-8 and Sentinel-2 time series data. Remote Sens., 11.","DOI":"10.3390\/rs11030328"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pastick, N.J., Wylie, B.K., and Wu, Z. (2018). Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems. Remote Sens., 10.","DOI":"10.3390\/rs10050791"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Skakun, S., Franch, B., Vermote, E., Roger, J., Justice, C., Masek, J., and Murphy, E. (2018, January 22\u201327). Winter wheat yield assessment using Landsat 8 and Sentinel-2 data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519134"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111124","DOI":"10.1016\/j.rse.2019.03.017","article-title":"Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series","volume":"238","author":"Griffiths","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1016\/S2095-3119(19)62599-2","article-title":"High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data","volume":"18","author":"Hao","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"J\u00f6nsson, P., Cai, Z., Melaas, E., Friedl, M., and Eklundh, L. (2018). A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sens., 10.","DOI":"10.3390\/rs10040635"},{"key":"ref_20","unstructured":"(2020, April 14). DIANA. Available online: http:\/\/diana-h2020.eu\/en\/."},{"key":"ref_21","first-page":"033515","article-title":"Automated registration and orthorectification package for Landsat and Landsat-like data processing","volume":"3","author":"Gao","year":"2009","journal-title":"JARS"},{"key":"ref_22","unstructured":"Tucker, C.J. (NASA-TM-79620, 1978). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, NASA-TM-79620."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"160118","DOI":"10.1038\/sdata.2016.118","article-title":"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_24","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_25","unstructured":"Mattiuzzi, M., Verbesselt, J., Stevens, F., Mosher, S., Hengl, T., Klisch, A., Evans, B., and Lobo, A. (2020, April 14). MODIS: MODIS Acquisition and Processing Package. Available online: http:\/\/R-Forge.R-project.org\/projects\/modis."},{"key":"ref_26","unstructured":"R Core Team (2013). R: A Language and Environment for Statistical Computing, R Core Team."},{"key":"ref_27","unstructured":"(2020, April 14). GDAL. Available online: https:\/\/gdal.org\/."},{"key":"ref_28","unstructured":"(2020, April 14). CHRS Data Portal. Available online: https:\/\/chrsdata.eng.uci.edu\/."},{"key":"ref_29","unstructured":"(2020, April 14). mapitGIS. Available online: https:\/\/mapitgis.com\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_31","first-page":"352","article-title":"A kernel function analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"54","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor non parametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_38","unstructured":"Wright, M.N., and Ziegler, A. (2015). ranger: A fast implementation of random forests for high dimensional data in C++ and R. arXiv."},{"key":"ref_39","unstructured":"Karatzoglou, A., Smola, A., and Hornik, K. (2020, April 14). Kernlab: Kernel-based Machine Learning Lab. Available online: https:\/\/cran.r-project.org\/web\/packages\/kernlab\/index.html."},{"key":"ref_40","unstructured":"Therneau, T., Atkinson, B., and Ripley, B. (2020, April 14). rpart: Recursive Partitioning and Regression Trees. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/index.html."},{"key":"ref_41","unstructured":"Kuhn, M., Weston, S., Coulter, N., and Quinlan, R. (2020, April 14). C50: C5.0 decision trees and rule-based models. Available online: http:\/\/CRAN.R-project.org\/packageC."},{"key":"ref_42","unstructured":"Ripley, B., and Venables, W. (2020, April 14). nnet: Feed-forward Neural Networks and Multinomial Log-linear Models. Available online: https:\/\/cran.r-project.org\/web\/packages\/nnet\/index.html."},{"key":"ref_43","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and R Core Team (2020, April 14). Caret: Classification and Regression Training. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/index.html."},{"key":"ref_44","first-page":"795","article-title":"Early history of the kappa statistic","volume":"41","author":"Smeeton","year":"1985","journal-title":"Biometrics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2017.12.001","article-title":"Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation","volume":"101","author":"Meyer","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1111\/2041-210X.13107","article-title":"blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models","volume":"10","author":"Valavi","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sharma, A.K., Hubert-Moy, L., Buvaneshwari, S., Sekhar, M., Ruiz, L., Bandyopadhyay, S., and Corgne, S. (2018). Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India. Remote Sens., 10.","DOI":"10.3390\/rs10060893"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Traor\u00e9, F., Bonkoungou, J., Compaor\u00e9, J., Kouadio, L., Wellens, J., Hallot, E., and Tychon, B. (2019). Using multi-temporal Landsat images and support vector machine to assess the changes in agricultural irrigated areas in the Mogtedo region, Burkina Faso. Remote Sens., 11.","DOI":"10.3390\/rs11121442"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, T., Deines, J.M., Kendall, A.D., Basso, B., and Hyndman, D.W. (2019). Addressing challenges for mapping irrigated fields in subhumid temperate regions by integrating remote sensing and hydroclimatic Data. Remote Sens., 11.","DOI":"10.4211\/hs.3766845be72d45969fca21530a67bb2d"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Demarez, V., Helen, F., Marais-Sicre, C., and Baup, F. (2019). In-season mapping of irrigated crops using Landsat 8 and Sentinel-1 time series. Remote Sens., 11.","DOI":"10.3390\/rs11020118"},{"key":"ref_52","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_53","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_54","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2017.05.041","article-title":"The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations","volume":"198","author":"Sadeghi","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1275\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:21:22Z","timestamp":1760361682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,17]]},"references-count":56,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081275"],"URL":"https:\/\/doi.org\/10.3390\/rs12081275","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,17]]}}}