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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 1-10     DOI: 10.6046/gtzyyg.2020189
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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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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.

Keywords cloud computing      Google Earth Engine      remote sensing      land cover      information extraction     
ZTFLH:  P23TP79  
Corresponding Authors: LI He     E-mail: 2804747677@qq.com;lih@lreis.ac.cn
Issue Date: 21 July 2021
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Xiaoli MOU
He LI
Chong HUANG
Qingsheng LIU
Gaohuan LIU
Cite this article:   
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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020189     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/1
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.
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  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
[1] 张磊, 吴炳方, 李晓松, 等. 基于碳收支的中国土地覆被分类系统[J]. 生态学报, 2014, 34(24):7158-7166.
[1] Zhang L, Wu B F, Li X S, et al. Classification system of China land cover for carbon budget[J]. Acta Ecologica Sinica, 2014, 34(24):7158-7166.
[2] 宫鹏, 张伟, 俞乐, 等. 全球地表覆盖制图研究新范式[J]. 遥感学报, 2016, 20(5):1002-1016.
[2] Gong P, Zhang W, Yu L, et al. New research paradigm for global land cover mapping[J]. Journal of Remote Sensing, 2016, 20(5):1002-1016.
[3] Chang J, Hansen M C, Pittman K, et al. Corn and soybean mapping in the United States using MODIS time-series data sets[J]. Agronomy Journal, 2007, 99:1654-1664.
doi: 10.2134/agronj2007.0170 url: https://onlinelibrary.wiley.com/doi/10.2134/agronj2007.0170
[4] Mutang O, Kumar L. Google Earth Engine applications[J]. Remote Sensing, 2019, 11(5):591.
doi: 10.3390/rs11050591 url: https://www.mdpi.com/2072-4292/11/5/591
[5] Hansen A M C, Potapov P V, Moore R, et al. Observing the forest and the trees:The first high resolution global maps of forest cover change[J]. Science, 2013, 342:850-853.
doi: 10.1126/science.1244693 pmid: 24233722
[6] Gong P, Liu H, Zhang M, et al. Stable classification with limited sample:Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373.
doi: 10.1016/j.scib.2019.03.002 url: https://linkinghub.elsevier.com/retrieve/pii/S2095927319301380
[7] Rudiyanto, Minasny B, Shah R M, et al. Automated near-real-time mapping and monitoring of rice extent,cropping patterns,and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform[J]. Remote Sensing, 2019, 11(14):1666.
doi: 10.3390/rs11141666 url: https://www.mdpi.com/2072-4292/11/14/1666
[8] Sun Z, Xu R, Du W, et al. High-resolution urban land mapping in China from Sentinel 1A/2 imagery based on Google Earth Engine[J]. Remote Sensing, 2019, 11(7):752.
doi: 10.3390/rs11070752 url: https://www.mdpi.com/2072-4292/11/7/752
[9] Ramdani I F. Recent expansion of oil palm plantation in the most eastern part of Indonesia:Feature extraction with polarimetric SAR[J]. International Journal of Remote Sensing, 2018, 40(19):7371-7388.
doi: 10.1080/01431161.2018.1508924 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1508924
[10] Xia H, Zhao J, Qin Y, et al. Changes in water surface area during 1989—2017 in the Huai River basin using Landsat data and Google Earth Engine[J]. Remote Sensing, 2019, 11(15):1824.
doi: 10.3390/rs11151824 url: https://www.mdpi.com/2072-4292/11/15/1824
[11] Padarian J, Minasny B, Mcbratney A B. Chile and the Chilean soil grid:A contribution to GlobalSoilMap[J]. Geoderma Regional, 2017(9):17-28.
[12] El-behaedi R, Ghoneim E. Flood risk assessment of the Abu Simbel temple complex (Egypt) based on high-resolution spaceborne stereo imagery[J]. Journal of Archaeological Science:Reports, 2018(20):458-467.
[13] Long T, Zhang Z, He G, et al. 30 m resolution global annual burned area mapping based on Landsat images and Google Earth Engine[J]. Remote Sensing, 2019, 11(5):489.
doi: 10.3390/rs11050489 url: http://www.mdpi.com/2072-4292/11/5/489
[14] Cao B, Domke G M, Russell M B, et al. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States[J]. The Science of the Total Environment, 2018(654):94-106.
[15] Campos-taberner M, Moreno-mart N Á, Javier G F, et al. Global estimation of biophysical variables from Google Earth Engine platform[J]. Remote Sensing, 2018, 10(8):1167.
doi: 10.3390/rs10081167 url: http://www.mdpi.com/2072-4292/10/8/1167
[16] Zhang Y, Kong D, Gan R, et al. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002—2017[J]. Remote Sensing of Environment, 2019(222):165-182.
[17] 刘畅, 李震, 张平, 等. 基于Google Earth Engine评估新疆西南部MODIS积雪产品[J]. 遥感技术与应用, 2018, 33(4):584-592.
[17] Liu C, Li Z, Zhang P, et al. Evaluation of MODIS snow products in southwestern Xinjiang using the Google Earth Engine[J]. Remote Sensing Technology and Application, 2018, 33(4):584-592.
[18] 郝斌飞, 韩旭军, 马明国, 等. Google Earth Engine在地球科学与环境科学中的应用研究进展[J]. 遥感技术与应用, 2018, 33(4):600-611.
[18] Hao B F, Han X J, Ma M G, et al. Research progress on the application of Google Earth Engine in geoscience and environmental sciences[J]. Remote Sensing Technology and Application, 2018, 33(4):600-611.
[19] Bascietto M, Bajocco S, Ferrrara C, et al. Estimating late spring frost-induced growth anomalies in European beech forests in Italy[J]. International Journal of Biometeorology, 2019, 63(8):1039-1049.
doi: 10.1007/s00484-019-01718-w pmid: 31065840
[20] Araujo N, Osei F, Leonardo L, et al. Modeling schistosoma japonicum infection under pure specification bias:Impact of environmental drivers of infection[J]. International Journal of Environmental Research and Public Health, 2019, 16(2):176.
doi: 10.3390/ijerph16020176 url: http://www.mdpi.com/1660-4601/16/2/176
[21] Sazib N, Mladenova I, Bolten J. Leveraging the Google Earth Engine for drought assessment using global soil moisture data[J]. Remote Sensing, 2018, 10(8):1265.
doi: 10.3390/rs10081265 url: http://www.mdpi.com/2072-4292/10/8/1265
[22] Lemoine G, Leo O. Crop mapping applications at scale:Using Google Earth Engine to enable global crop area and status monitoring using free and open data sources[C]// 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).New York:IEEE, 2015:1496-1499.
[23] Dong J, Xiao X, Menarguez M A, et al. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images,phenology-based algorithm and Google Earth Engine[J]. Remote Sensing of Environment, 2016(185):142-154.
[24] Lobell D B, Thau D, Seifert C, et al. A scalable satellite-based crop yield mapper[J]. Remote Sensing of Environment, 2015(164):324-333.
[25] Aneece I, Thenkabail P. Accuracies achieved in classifying five leading world crop types and their growth stages using Optimal Earth Observing-1 Hyperion hyperspectral narrowbands on Google Earth Engine[J]. Remote Sensing, 2018, 10(12):2027
doi: 10.3390/rs10122027 url: http://www.mdpi.com/2072-4292/10/12/2027
[26] Zhang X, Wu B, Ponce-campos G, et al. Mapping up-to-date paddy rice extent at 10 m resolution in China through the integration of optical and synthetic aperture Radar images[J]. Remote Sensing, 2018, 10(8):1200.
doi: 10.3390/rs10081200 url: http://www.mdpi.com/2072-4292/10/8/1200
[27] Vogels M, De Jong S, Sterk G, et al. Spatio-temporal patterns of smallholder irrigated agriculture in the horn of Africa using GEOBIA and Sentinel-2 imagery[J]. Remote Sensing, 2019, 11(2):143.
doi: 10.3390/rs11020143 url: http://www.mdpi.com/2072-4292/11/2/143
[28] Aguilar R, Zurita-Milla R, Izquierdo-Verdiguier E, et al. A cloud-based multi-temporal ensemble classifier to map smallholder farming systems[J]. Remote Sensing, 2018, 10(5):729.
doi: 10.3390/rs10050729 url: http://www.mdpi.com/2072-4292/10/5/729
[29] Jin Z, Azzari G, Lobell D B. Improving the accuracy of satellite-based high-resolution yield estimation:A test of multiple scalable approaches[J]. Agricultural and Forest Meteorology, 2017(247):207-220.
[30] Poortinga A, Tenneson K, Shapiro A, et al. Mapping plantations in Myanmar by fusing Landsat-8,Sentinel-2 and Sentinel-1 data along with systematic error quantification[J]. Remote Sensing, 2019, 11(7):831.
doi: 10.3390/rs11070831 url: https://www.mdpi.com/2072-4292/11/7/831
[31] Tsai Y, Stow D, Chen H, et al. Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine[J]. Remote Sensing, 2018, 10(6):927.
doi: 10.3390/rs10060927 url: http://www.mdpi.com/2072-4292/10/6/927
[32] Workie T G, Debella H J. Climate change and its effects on vegetation phenology across ecoregions of Ethiopia[J]. Global Ecology and Conservation, 2018, 13:e00366.
doi: 10.1016/j.gecco.2017.e00366 url: https://linkinghub.elsevier.com/retrieve/pii/S2351989417301646
[33] Shrestha S, Miranda I, Kumar A, et al. Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 74:281-294.
doi: 10.1016/j.jag.2018.09.017 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243418302940
[34] Pereira O, Ferreira L, Pinto F, et al. Assessing pasture degradation in the Brazilian cerrado based on the analysis of MODIS NDVI time-series[J]. Remote Sensing, 2018, 10(11):1761.
doi: 10.3390/rs10111761 url: https://www.mdpi.com/2072-4292/10/11/1761
[35] Soulard C, Albano C, Villarrreal M, et al. Continuous 1985—2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park,California[J]. Remote Sensing, 2016, 8(5):371.
doi: 10.3390/rs8050371 url: http://www.mdpi.com/2072-4292/8/5/371
[36] Wang J, Xiao X, Qin Y, et al. Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984—2010 through PALSAR and time series Landsat images[J]. Remote Sensing of Environment, 2017, 190:233-246.
doi: 10.1016/j.rse.2016.12.025 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425716305041
[37] Chen B, Jin Y, Brown P. Automatic mapping of planting year for tree crops with Landsat satellite time series stacks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151:176-188.
doi: 10.1016/j.isprsjprs.2019.03.012 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271619300802
[38] Cao B, Domke G M, Russell M B, et al. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States[J]. The Science of the Total Environment, 2018, 654:94-106.
doi: 10.1016/j.scitotenv.2018.10.359 url: https://linkinghub.elsevier.com/retrieve/pii/S0048969718342591
[39] Goldblatt R, Stuhlmacher M F, Tellman B, et al. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover[J]. Remote Sensing of Environment, 2018, 205:253-275.
doi: 10.1016/j.rse.2017.11.026 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717305758
[40] Liu X, Hu G, Chen Y, et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine platform[J]. Remote Sensing of Environment, 2018, 209:227-239.
doi: 10.1016/j.rse.2018.02.055 url: https://linkinghub.elsevier.com/retrieve/pii/S003442571830066X
[41] Parastatidis D, Mitraka Z, Chrysoulakis N, et al. Online global land surface temperature estimation from Landsat[J]. Remote Sensing, 2017, 9(12):1208.
doi: 10.3390/rs9121208 url: http://www.mdpi.com/2072-4292/9/12/1208
[42] Chakraborty T, Lee X. A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 74:269-280.
doi: 10.1016/j.jag.2018.09.015 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243418304653
[43] Ravanelli R, Nascetti A, Cirigliano R, et al. Monitoring the impact of land cover change on surface urban heat island through Google Earth Engine:Proposal of a global methodology,first applications and problems[J]. Remote Sensing, 2018, 10(9):1488.
doi: 10.3390/rs10091488 url: http://www.mdpi.com/2072-4292/10/9/1488
[44] Huang C, Yang J, Jiang P. Assessing impacts of urban form on landscape structure of urban green spaces in China using Landsat images based on Google Earth Engine[J]. Remote Sensing, 2018, 10(10):1569.
doi: 10.3390/rs10101569 url: http://www.mdpi.com/2072-4292/10/10/1569
[45] Huang C, Yang J, Lu H, et al. Green spaces as an indicator of urban health:Evaluating its changes in 28 mega-cities[J]. Remote Sensing, 2017, 9(12):1266.
doi: 10.3390/rs9121266 url: http://www.mdpi.com/2072-4292/9/12/1266
[46] Busker T, De R A, Gelati E. A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry[J]. Hydrology and Earth System Sciences, 2019, 23(2):669-690.
doi: 10.5194/hess-23-669-2019
[47] Zhang M M, Chen F, Tian B S. An automated method for glacial lake mapping in high mountain Asia using Landsat 8 imagery[J]. Journal of Mountain Science, 2018, 15(1):13-24.
doi: 10.1007/s11629-017-4518-5 url: http://link.springer.com/10.1007/s11629-017-4518-5
[48] Beaton A, Whaley R, Corston K, et al. Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario[J]. Remote Sensing of Environment, 2019, 224:352-364.
doi: 10.1016/j.rse.2019.02.011
[49] Wayand N E, Marsh C B, Shea J M, et al. Globally scalable alpine snow metrics[J]. Remote Sensing of Environment, 2018, 213:61-72.
doi: 10.1016/j.rse.2018.05.012 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425718302335
[50] Zhang H, Gorelick S M, Zimba P V, et al. A remote sensing method for estimating regional reservoir area and evaporative loss[J]. Journal of Hydrology, 2017, 555:213-227.
doi: 10.1016/j.jhydrol.2017.10.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169417306716
[51] Huang Q, Long D, Du M, et al. Discharge estimation in high-mountain regions with improved methods using multisource remote sensing:A case study of the Upper Brahmaputra River[J]. Remote Sensing of Environment, 2018, 219:115-134.
doi: 10.1016/j.rse.2018.10.008 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425718304565
[52] Griffin C G, Mcclelland J W, Frey K E, et al. Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM+ data[J]. Remote Sensing of Environment, 2018, 209:395-409.
doi: 10.1016/j.rse.2018.02.060 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425718300725
[53] 周志立. 基于GEE平台的近十年来洪湖水质遥感反演研究[D]. 武汉:湖北大学, 2017.
[53] Zhou Z L. Remote sensing inversion research of Honghu water quality based on GEE platform in recent ten years[D]. Wuhan:Hubei University, 2017.
[54] Huang H, Chen Y, Clinton N, et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine[J]. Remote Sensing of Environment, 2017, 202:166-176.
doi: 10.1016/j.rse.2017.02.021 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717300810
[55] Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine:Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202:18-27.
doi: 10.1016/j.rse.2017.06.031 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717302900
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