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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 1-10     DOI: 10.6046/gtzyyg.2020189
Application progress of Google Earth Engine in land use and land cover remote sensing information extraction
MOU Xiaoli1,2(), LI He1(), HUANG Chong1, LIU Qingsheng1, LIU Gaohuan1
1. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China
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Google Earth Engine is a cloud-based, global-scale geospatial analysis platform that makes full use of Google Earth’s rich data resources and cloud computing power to store and process petabyte-level data, being an effective and convenient tool for remote sensing research. Based on the introduction of Google Earth Engine system architecture, the authors firstly sorted out the research fields of Google Earth Engine. 291 related articles on CNKI and Web of Science published from 2011 to 2019 were analyzed, and some results were concluded such as publication time, research field, research area, the first author’s institution and journal of the article. Then the authors analyzed Google Earth Engine’s application and research trends of land use and land cover. The authors found that Google Earth Engine is widely used in the field of land cover remote sensing information extraction and has advantages in global or large-scale study. Based on the advantages of Google Earth Engine in remote sensing information extraction, the authors divided the study fields into agricultural remote sensing mapping, vegetation extent mapping and dynamic monitoring, building extraction, hydrological information extraction and land cover classification mapping. The research and application progress of Google Earth Engine was elaborated from two aspects: large-area mapping and multi-temporal dynamic monitoring. Finally, the authors discussed the Google Earth Engine’s problems and the development potential in land use and land cover. This paper is intended to serve as a basis for further understanding the advantages, application status, trends and potential of Google Earth Engine as well as for further understanding and using Google Earth Engine in the future.

Keywords cloud computing      Google Earth Engine      remote sensing      land cover      information extraction     
ZTFLH:  P23TP79  
Corresponding Authors: LI He     E-mail:;
Issue Date: 21 July 2021
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Xiaoli MOU
Qingsheng LIU
Gaohuan LIU
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Xiaoli MOU,He LI,Chong HUANG, et al. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction[J]. Remote Sensing for Land & Resources, 2021, 33(2): 1-10.
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Fig.1  System architecture diagram of Google Earth Engine
Fig.2  Code editor of Google Earth Engine
卫星 数据集 空间分辨率/m 重访周期/d 数据的可用性(时间) 供应商 论文数/篇
Landsat Landsat4—8 表面反射率 30 16 1984至今 USGS/NASA 177
Landsat 4 TM 30 16 1982.08.22—1993.
Landsat 5 TM 30 16 1984.01.01—2012.
Landsat 7 ETM 30 16 1999.01.01至今
Landsat 8 OLI/TIRS 30 16 2013.04.11至今
Sentinel Sentinel-1 SAR GRD 10 33 2014.10.04至今 欧洲联盟/欧洲空间局/哥白尼计划 58
Sentinel-2 MSI 10,20,60 5 2015.06.23至今
Sentinel-3 OLCI EFR 300 2 2016.10.18至今
MODIS MODIS(Aqua和Terra) 250,500,1 000 1 2000.02.24至今 LP DAAC/NASA 38
MOD13 植被指数 250,500 16 2000.02.25至今
MOD09表面反射率 500 1 2000.02.26至今
MOD10雪覆盖 500 1 2000.02.27至今
ASTER ASTER 15,30,90 5 2000.03.04至今 LP DAAC/NASA 7
SRTM DEM 30 30 2000.02.11—2000.
NASA/USGS/Jet Propulsion Laboratory-加州理工 11
DMSP DMSP OLS 1 000 1992.01.01—2014.
National Oceanic and Atmospheric Administration 5
EO-1 EO-2 Hyperion 30 2001至今 2
Tab.1  Common datasets in Google Earth Engine
Fig.3  Timeline of Google Earth Engine
Fig.4  Global land cover products(FROM-GLC10) with 10 m spatial resolution
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