{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:30:16Z","timestamp":1771036216651,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Program of National Natural Science Foundation of China","award":["41971234"],"award-info":[{"award-number":["41971234"]}]},{"name":"the Program of National Natural Science Foundation of China","award":["41971235"],"award-info":[{"award-number":["41971235"]}]},{"name":"the Program of National Natural Science Foundation of China","award":["41901231"],"award-info":[{"award-number":["41901231"]}]},{"name":"Nanjing University Inovation and Creative Program for PhD Candidate","award":["CXYJ21-45"],"award-info":[{"award-number":["CXYJ21-45"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai\u2019an County, Rudong County and Pizhou City.<\/jats:p>","DOI":"10.3390\/rs13071245","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T21:09:45Z","timestamp":1616706585000},"page":"1245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2250-1282","authenticated-orcid":false,"given":"Jinhuang","family":"Lin","sequence":"first","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"},{"name":"Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1862-5837","authenticated-orcid":false,"given":"Xiaobin","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"},{"name":"Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"},{"name":"Natural Resources Research Center, 163 Xianlin Avenue, Qixia District, Nanjing University, Nanjing 210023, China"}]},{"given":"Jie","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"}]},{"given":"Jingping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2903-0062","authenticated-orcid":false,"given":"Xinyuan","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"}]},{"given":"Yinkang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"},{"name":"Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China"},{"name":"Natural Resources Research Center, 163 Xianlin Avenue, Qixia District, Nanjing University, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.biosystemseng.2016.10.018","article-title":"Analysis of the collapse of a greenhouse with vaulted roof","volume":"151","author":"Briassoulis","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.landurbplan.2010.11.008","article-title":"Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques","volume":"100","author":"Picuno","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/01431160600658156","article-title":"Remote sensing as a tool for monitoring plasticulture in agricultural landscapes","volume":"28","author":"Levin","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1016\/j.polymdegradstab.2012.06.024","article-title":"Experimental tests and technical characteristics of regenerated films from agricultural plastics","volume":"97","author":"Picuno","year":"2012","journal-title":"Polym. Degrad. Stab."},{"key":"ref_6","unstructured":"National Bureau Statistics (2017, December 15). Bulletin of Main Data of the Third National Agricultural Census, Available online: http:\/\/www.stats.gov.cn\/tjsj\/tjgb\/nypcgb\/qgnypcgb\/201712\/t20171215_1563539.html."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.compag.2017.07.003","article-title":"Agricultural plastic waste spatial estimation by Landsat 8 satellite images","volume":"141","author":"Lanorte","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2017.03.002","article-title":"Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index","volume":"128","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","first-page":"173","article-title":"Plastic greenhouse recognition based on GF-2 data and multi-texture features","volume":"35","author":"Wu","year":"2019","journal-title":"Trans. CSAE"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Aguilar, M.\u00c1., Jim\u00e9nez-Lao, R., Nemmaoui, A., Aguilar, F.J., Koc-San, D., Tarantino, E., and Chourak, M. (2020). Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses. Remote Sens., 12.","DOI":"10.3390\/rs12122015"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"073553","DOI":"10.1117\/1.JRS.7.073553","article-title":"Evaluation of different classifification 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_14","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/JSTARS.2019.2950466","article-title":"Mapping Plastic Greenhouses Using Spectral Metrics Derived from GaoFen-2 Satellite Data","volume":"13","author":"Shi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.isprsjprs.2008.03.003","article-title":"Using texture analysis to improve perpixel 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_16","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_17","first-page":"43","article-title":"The development of plastic greenhouse index based on Logistic regression analysis","volume":"31","author":"Chen","year":"2019","journal-title":"Remote Sens. Land Resour."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for cop mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and google earth engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using google earth engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aguilar, R., Zurita-Milla, R., Izquierdo-Verdiguier, E., and de By, R.A. (2018). A cloud-based multi-temporal ensemble classifier to map smallholder farming systems. Remote Sens., 10.","DOI":"10.3390\/rs10050729"},{"key":"ref_23","first-page":"752","article-title":"Extraction of summer crop in Jiangsu based on Google Earth Engine","volume":"21","author":"He","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_24","first-page":"2507","article-title":"Review of features selection in crop classification using remote sensing data","volume":"35","author":"Jia","year":"2013","journal-title":"Resour. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s13157-014-0542-1","article-title":"Spatio-temporal dynamics of wetland landscape patterns based on remote sensing in yellow river delta, China","volume":"34","author":"Liu","year":"2014","journal-title":"Wetlands"},{"key":"ref_26","first-page":"248","article-title":"Object-oriented land use\/cover classification based on texture features of Landsat 8 OLI image","volume":"34","author":"Pei","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_27","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_28","first-page":"190","article-title":"Texture feature extraction analysis of remote sensing image based on gray level co-occurrence matrix","volume":"22","author":"Zhang","year":"2017","journal-title":"Cult. Geogr."},{"key":"ref_29","first-page":"582","article-title":"Classification and regression trees","volume":"40","author":"Breiman","year":"1984","journal-title":"Encycl. Ecol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0218001418590127","article-title":"A new technique for remote sensing image classification based on combinatorial algorithm of svm and knn","volume":"32","author":"Alimjan","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"Uav remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/36.823938","article-title":"An approach to feature selection and classification of remote sensing images based on the bayes rule for minimum cost","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","first-page":"273","article-title":"Land use classification in coal mining area using remote sensing images based on multiple classifier combination","volume":"40","author":"Chen","year":"2011","journal-title":"J. China Univ. Min. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.046023","article-title":"Accuracy assessment model for classification result of remote sensing image based on spatial sampling","volume":"11","author":"Huang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","first-page":"168","article-title":"Spatial-temporal dynamic changes of agricultural greenhouses in Shandong Province in recent 30 years based on Google Earth Engine","volume":"51","author":"Zhu","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_37","first-page":"677","article-title":"Retrievalof Agriculture Greenhouse based on GF-2 Remote Sensing Images","volume":"34","author":"Zhao","year":"2019","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_38","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:47Z","timestamp":1760161247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071245"],"URL":"https:\/\/doi.org\/10.3390\/rs13071245","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}