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国土资源遥感  2021, Vol. 33 Issue (2): 1-10    DOI: 10.6046/gtzyyg.2020189
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Google Earth Engine在土地覆被遥感信息提取中的研究进展
牟晓莉1,2(), 李贺1(), 黄翀1, 刘庆生1, 刘高焕1
1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2.中国地质大学(北京)地球科学与资源学院,北京 100083
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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摘要 

谷歌地球引擎(Google Earth Engine,GEE)是一个面向全球尺度的地理空间分析平台,充分集成了Google Earth海量的地理和遥感数据资源以及Google的强大云端计算能力,为地球系统科学、特别是其重要组成部分的土地覆被遥感信息提取研究提供了一种有效便捷的方式。围绕GEE和土地覆被遥感信息提取相关的关键词,查阅了Web of Science和知网在2011—2019年间国内外发表的所有相关论文,在统计文献发表时间、研究领域、研究区、所属机构和发表期刊等信息的基础上,系统梳理了GEE在土地覆被领域的研究应用趋势,重点就大区域制图和多时相变化监测两方面,详细阐述了GEE的应用发展潜力,为进一步认识和使用GEE提供了科学参考。

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牟晓莉
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关键词 云计算Google Earth Engine遥感土地覆被信息提取    
Abstract

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.

Key wordscloud computing    Google Earth Engine    remote sensing    land cover    information extraction
收稿日期: 2020-06-29      出版日期: 2021-07-21
ZTFLH:  P23TP79  
基金资助:国家自然科学基金青年科学基金项目“集合四维变分同化叶面积指数和蒸散发的区域冬小麦产量估测”(41801353);国家重点研发计划科技基础资源调查专项“红树林资源遥感监测与评估”(2017FY100706);国家重点研发计划“固废资源化”重点专项“张家港市固废园区化协同处置技术开发与集成示范”(2018YFC1903000)
通讯作者: 李贺
作者简介: 牟晓莉(1996-),女,硕士研究生,主要从事资源环境遥感研究。Email: 2804747677@qq.com
引用本文:   
牟晓莉, 李贺, 黄翀, 刘庆生, 刘高焕. Google Earth Engine在土地覆被遥感信息提取中的研究进展[J]. 国土资源遥感, 2021, 33(2): 1-10.
MOU Xiaoli, LI He, HUANG Chong, LIU Qingsheng, LIU Gaohuan. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction. Remote Sensing for Land & Resources, 2021, 33(2): 1-10.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020189      或      https://www.gtzyyg.com/CN/Y2021/V33/I2/1
Fig.1  Google Earth Engine系统架构图
Fig.2  Google Earth Engine 代码编辑界面
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.
12.14
Landsat 5 TM 30 16 1984.01.01—2012.
05.01
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.
02.22
NASA/USGS/Jet Propulsion Laboratory-加州理工 11
DMSP DMSP OLS 1 000 1992.01.01—2014.
01.01
National Oceanic and Atmospheric Administration 5
EO-1 EO-2 Hyperion 30 2001至今 2
Tab.1  Google Earth Engine常用数据集
Fig.3  Google Earth Engine 发展历程时间轴
Fig.4  10 m空间分辨率的全球土地覆被产品(FROM-GLC10)
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