{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T18:05:19Z","timestamp":1784829919859,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,15]],"date-time":"2019-12-15T00:00:00Z","timestamp":1576368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFA0603001"],"award-info":[{"award-number":["2017YFA0603001"]}]},{"name":"the Key Research Program of the Chinese Academy of Sciences","award":["ZDRW-ZS-2019-1"],"award-info":[{"award-number":["ZDRW-ZS-2019-1"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41825002"],"award-info":[{"award-number":["41825002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere\u2013Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral\u2013temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 \u00b1 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.<\/jats:p>","DOI":"10.3390\/rs11243023","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"3023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine"],"prefix":"10.3390","volume":"11","author":[{"given":"Shuai","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-037X","authenticated-orcid":false,"given":"Liangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangning","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xidong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,15]]},"reference":[{"key":"ref_1","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. 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