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Based on the self-programming in Google Earth Engine (GEE) cloud-based platform, a large and reliable training dataset of multispectral Sentinel-2 image was extracted automatically across the study area from the existing MODIS land cover product. To enhance confidence in high-quality training class labels, homogeneous 20 m Sentinel-2 pixels within each 500 m MODIS pixel were selected and a minority of heterogeneous 20 m pixels were removed based on calculations of spectral centroid and Euclidean distance. Further, the quality control and spatial filter were applied for all land cover classes to generate a reliable and representative training dataset that was subsequently applied to train the Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM) classifiers. The results shows that the main land cover types in the study area as distinguished by three different classifiers were Evergreen Broadleaf Forests, Mixed Forests, Woody Savannas, and Croplands. In the training and validation samples, the numbers of correctly classified pixels under the CART without computationally intensive were more than those for the RF and SVM classifiers. Moreover, the user\u2019s and producer\u2019s accuracies, overall accuracy and kappa coefficient of the CART classifier were the best, indicating the CART classifier was more suitable to this automatic workflow for land cover mapping. The proposed method can automatically generate a large number of reliable and accurate training samples in a timely manner, which is promising for future land cover mapping in a large-scale region.<\/jats:p>","DOI":"10.1371\/journal.pone.0312585","type":"journal-article","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T16:59:49Z","timestamp":1744045189000},"page":"e0312585","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated mapping of land cover in Google Earth Engine platform using multispectral Sentinel-2 and MODIS image 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probabilistic wetland mapping","volume":"9","author":"J Hird","year":"2017","journal-title":"Remote Sens"},{"issue":"10","key":"pone.0312585.ref022","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.3390\/rs10101509","article-title":"Google Earth Engine applications since inception: usage, trends, and potential","volume":"10","author":"L Kumar","year":"2018","journal-title":"Remote Sens"},{"issue":"7","key":"pone.0312585.ref023","doi-asserted-by":"crossref","first-page":"842","DOI":"10.3390\/rs11070842","article-title":"Canadian wetland inventory using Google Earth Engine: the first map and preliminary results","volume":"11","author":"M Amani","year":"2019","journal-title":"Remote Sens"},{"issue":"6160","key":"pone.0312585.ref024","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"MC 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Intell"},{"issue":"3","key":"pone.0312585.ref034","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: a review","volume":"66","author":"G Mountrakis","year":"2011","journal-title":"ISPRS J Photogram Remote Sens"},{"key":"pone.0312585.ref035","first-page":"100235","article-title":"Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques","volume":"15","author":"G Singhal","year":"2019","journal-title":"Remote Sens Appl"},{"key":"pone.0312585.ref036","doi-asserted-by":"crossref","first-page":"111933","DOI":"10.1016\/j.rse.2020.111933","article-title":"Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method","volume":"247","author":"Y Loozen","year":"2020","journal-title":"Remote Sens Environ"},{"key":"pone.0312585.ref037","first-page":"88","article-title":"Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics","volume":"73","author":"L Hu","year":"2018","journal-title":"Int J Appl Earth Obse Geoinf"},{"issue":"19","key":"pone.0312585.ref038","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.3390\/rs12193120","article-title":"Advancing the mapping of mangrove forests at national-scale using Sentinel-1 and Sentinel-2 time-series data with Google Earth Engine: a case study in China","volume":"12","author":"L Hu","year":"2020","journal-title":"Remote Sens"},{"issue":"1","key":"pone.0312585.ref039","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":"Y Qian","year":"2014","journal-title":"Remote Sens"},{"key":"pone.0312585.ref040","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ecoser.2018.05.001","article-title":"Estimating the impacts of land use\/land cover changes on ecosystem service values: the case of the Andassa watershed in the Upper Blue Nile basin of Ethiopia","volume":"31","author":"T Gashaw","year":"2018","journal-title":"Ecosyst Serv"},{"issue":"19","key":"pone.0312585.ref041","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.3390\/rs12193139","article-title":"Land use\/land cover changes and their driving factors in the northeastern Tibetan plateau based on geographical detectors and Google Earth Engine: a case study in Gannan prefecture","volume":"12","author":"C Liu","year":"2020","journal-title":"Remote Sens"},{"issue":"17","key":"pone.0312585.ref042","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.3390\/rs12172817","article-title":"Comparison of machine-learning methods for urban land-use mapping in Hangzhou city, China","volume":"12","author":"W Mao","year":"2020","journal-title":"Remote Sens"},{"key":"pone.0312585.ref043","first-page":"181","article-title":"Comparison of machine learning method in object-based image classification.","volume":"41","author":"D Zhao","year":"2016","journal-title":"Sci Surv Map"},{"issue":"7","key":"pone.0312585.ref044","doi-asserted-by":"crossref","first-page":"750","DOI":"10.3390\/f11070750","article-title":"Quantifying land cover changes in a Mediterranean environment using Landsat TM and support vector machines","volume":"11","author":"S Fragou","year":"2020","journal-title":"Forests"},{"issue":"18","key":"pone.0312585.ref045","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.3390\/rs12183019","article-title":"Comparison of machine learning methods for mapping the stand characteristics of temperate forests using multi-spectral Sentinel-2 data","volume":"12","author":"K Ahmadi","year":"2020","journal-title":"Remote Sens"},{"issue":"7","key":"pone.0312585.ref046","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.77.7.721","article-title":"A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery","volume":"77","author":"X Huang","year":"2011","journal-title":"Photogramm Eng Remote Sens"},{"key":"pone.0312585.ref047","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: a review of applications and future directions","volume":"114","author":"M Belgiu","year":"2016","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"pone.0312585.ref048","unstructured":"Henrich V, G\u00f6tze E, Jung A, Sandow C, Th\u00fcrkow D, Gl\u00e4\u00dfer C. Development of an online indices database: Motivation, concept and implementation. In Proceedings of the 6th EARSeL Imaging Spectroscopy SIG Workshop Innovative Tool for Scientific and Commercial Environment Applications; 2009 March 16\u201318; Tel Aviv, Israel; p. 16\u201318."},{"issue":"3","key":"pone.0312585.ref049","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap","author":"EP Crist","year":"1984","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"pone.0312585.ref050","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1046\/j.0028-646X.2001.00289.x","article-title":"An evaluation of noninvasive methods to estimate foliar chlorophyll content","volume":"153","author":"AD Richardson","year":"2002","journal-title":"New Phytologist"},{"key":"pone.0312585.ref051","unstructured":"Alkhaier F. Soil Salinity Detection Using Satellite Remote Sensing. 2003."}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T17:02:05Z","timestamp":1744045325000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0312585"}},"subtitle":[],"editor":[{"given":"Bijay","family":"Halder,","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,4,7]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4,7]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0312585","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,7]]}}}