{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:53:07Z","timestamp":1775191987450,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"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>Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML\u2019s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the\u2014Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1\u20133%.<\/jats:p>","DOI":"10.3390\/rs10111820","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T11:48:31Z","timestamp":1542368911000},"page":"1820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1701-3046","authenticated-orcid":false,"given":"Lizhen","family":"Lu","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Yuan","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Liping","family":"Di","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Introduction","volume":"Volume 1","author":"Takakura","year":"2002","journal-title":"Climate under Cover-Digital Dynamic Simulation in Plant Bio-Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4548","DOI":"10.1109\/JSTARS.2014.2327226","article-title":"A Decision-Tree Classifier for Extracting Transparent Plastic-Mulched Landcover from Landsat-5 TM Images","volume":"7","author":"Lu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"097094","DOI":"10.1117\/1.JRS.9.097094","article-title":"Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: A case study in southern Xinjiang, China","volume":"9","author":"Lu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_4","first-page":"9","article-title":"Analysis of situations of China agro-film industry (2010) and countermeasures for its development","volume":"24","author":"Zhou","year":"2010","journal-title":"China Plast."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.isprsjprs.2008.03.003","article-title":"Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses","volume":"63","author":"Aguilar","year":"2008","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"073553","DOI":"10.1117\/1.JRS.7.073553","article-title":"Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery","volume":"7","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/2150704X.2015.1093186","article-title":"Combining ad hoc spectral indices based on LANDSAT-8 OLI\/TIRS sensor data for the detection of plastic cover vineyard","volume":"6","author":"Novelli","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3390\/rs8040353","article-title":"Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features","volume":"8","author":"Chen","year":"2016","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3390\/rs9060557","article-title":"Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data","volume":"9","author":"Chen","year":"2017","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3390\/rs9030265","article-title":"Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery","volume":"9","author":"Chen","year":"2017","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.3390\/rs4071913","article-title":"Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data","volume":"4","author":"Tarantino","year":"2012","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"046017","DOI":"10.1117\/1.JRS.12.046017","article-title":"Large-scale subpixel mapping of landcover from MODIS imagery using the improved spatial attraction model","volume":"12","author":"Lu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3554","DOI":"10.3390\/rs6053554","article-title":"Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery","volume":"6","author":"Aguilar","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aguilar, M., Nemmaoui, A., Novelli, A., Aguilar, F., and Garc\u00eda Lorca, A. (2016). Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8060513"},{"key":"ref_15","first-page":"403","article-title":"Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almer\u00eda (Spain)","volume":"52","author":"Novelli","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.5194\/isprs-archives-XLI-B7-145-2016","article-title":"Assessment of multiresolution segmentation for extracting greenhouses from WorldView-2 imagery","volume":"XLI-B7","author":"Aguilar","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7378","DOI":"10.3390\/rs70607378","article-title":"Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: A case study in Almeria, Spain","volume":"7","author":"Aguilar","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","first-page":"79","article-title":"Object-based classification approach for greenhouse mapping using Landsat-8 imagery","volume":"9","author":"Wu","year":"2016","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isprsjprs.2008.07.006","article-title":"Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories","volume":"64","author":"McNairn","year":"2009","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.3390\/rs9121264","article-title":"Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data","volume":"9","author":"Chen","year":"2017","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"(2018, June 28). Sentinel-1 Product Definition. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/1877131\/Sentinel-1-Product-Definition."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Small, D., and Schubert, A. (2008). Guide to ASAR Geocoding, The University of Zurich. RSL-ASAR-GC-AD."},{"key":"ref_25","unstructured":"Muller-Wilm, U., Louis, J., Richter, R., Gascon, F., and Niezette, M. (2013, January 9\u201313). Sentinel-2 Level 2A Prototype Processor: Architecture, Algorithms and First Results. Proceedings of the ESA Living Planet Symposium, Edinburgh, UK."},{"key":"ref_26","unstructured":"(2018, June 28). The Sentinel Application Platform (SNAP). Available online: http:\/\/step.esa.int\/main\/download."},{"key":"ref_27","unstructured":"Strobl, J., and Blaschke, T. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1080\/01431160701241746","article-title":"Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition","volume":"28","author":"Tian","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Dragut","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.geomorph.2011.12.001","article-title":"Automated object-based classification of topography from SRTM data","volume":"141","author":"Dragut","year":"2012","journal-title":"Geomorphology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Dragut","year":"2014","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Novelli, A., Aguilar, M.A., Aguilar, F.J., Nemmaoui, A., and Tarantino, E. (2017). AssesSeg\u2014A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9010040"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Accuracy assessment measures for object-based image segmentation goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Novelli, A., Aguilar, M.A., Aguilar, F.J., Nemmaoui, A., and Tarantino, E. (2017). C_AssesSeg Concurrent Computing Version of AssesSeg: A Benchmark between the New and Previous Version. Computational Science and Its Applications\u2014ICCSA 2017, Proceedings of the International Conference on Computational Science and Its Applications, Trieste, Italy, 3\u20136 July 2017, Springer.","DOI":"10.1007\/978-3-319-62401-3_4"},{"key":"ref_37","unstructured":"Trimble Germany GmbH (2014). eCognition Developer 9.0.1 Reference Book, Trimble Germany GmbH."},{"key":"ref_38","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third ERTS Symposium, NASA SP-351, Washington, DC, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1560\/IJPS.60.1-2.65","article-title":"Separability of maize and soybean in the spectral regions of chlorophyll and carotenoids using the moment distance index","volume":"60","author":"Salas","year":"2012","journal-title":"Isr. J. Plant Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Salas, E.A.L., Boykin, K.G., and Valdez, R. (2016). Multispectral and texture feature application in image-object analysis of summer vegetation in Eastern Tajikistan Pamirs. Remote Sens., 8.","DOI":"10.3390\/rs8010078"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kaszta, Z., Kerchove, R.V.D., Ramoelo, A., Cho, M.A., Madonsela, S., Mathieu, R., and Wolff, E. (2016). Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms. Remote Sens., 8.","DOI":"10.3390\/rs8090763"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12419","DOI":"10.3390\/rs70912419","article-title":"Mapping Urban Areas with Integration of DMSP\/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques","volume":"7","author":"Jing","year":"2015","journal-title":"Remote Sens."},{"key":"ref_44","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth & Brooks."},{"key":"ref_45","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_46","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_47","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_48","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for Land Cover Classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","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_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","unstructured":"Wang, X., Liu, S., Du, P., Liang, H., Xia, J., and Li, Y. (2018). Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sens., 10.","DOI":"10.3390\/rs10020276"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1820\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:14Z","timestamp":1760196614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1820"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,16]]},"references-count":51,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111820"],"URL":"https:\/\/doi.org\/10.3390\/rs10111820","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,16]]}}}