{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:19:21Z","timestamp":1781619561598,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T00:00:00Z","timestamp":1706918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3000400"],"award-info":[{"award-number":["2021YFC3000400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial\u2013spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.<\/jats:p>","DOI":"10.3390\/rs16030583","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T09:31:58Z","timestamp":1707125518000},"page":"583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Remote Sensing Extraction of Lakes on the Tibetan Plateau Based on the Google Earth Engine and Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunxuan","family":"Pang","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-0504","authenticated-orcid":false,"given":"Junchuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laidian","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daqing","family":"Ge","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2120-434X","authenticated-orcid":false,"given":"Ping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhong","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"},{"name":"School of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"},{"name":"Technology Innovation Center for Geohazard Identification and Monitoring with Earth Observation System, MNR, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8273-8042","authenticated-orcid":false,"given":"Liu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, TAS 7005, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1038\/s43017-020-0067-5","article-title":"Global lake responses to climate change","volume":"1","author":"Woolway","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.scitotenv.2007.06.041","article-title":"Environmental response to climate and human impact during the last 400 years in Taibai Lake catchment, middle reach of Yangtze River, China","volume":"385","author":"Liu","year":"2007","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115581","DOI":"10.1016\/j.jenvman.2022.115581","article-title":"Land use, hydrology, and climate influence water quality of China\u2019s largest river","volume":"318","author":"Xiong","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1017\/S0376892902000036","article-title":"Large freshwater lakes: Present state, trends, and future","volume":"29","author":"Beeton","year":"2002","journal-title":"Environ. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/250213a0","article-title":"Remote sensing and lake eutrophication","volume":"250","author":"Wrigley","year":"1974","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s40010-017-0428-8","article-title":"Satellite remote sensing: Sensors, applications and techniques","volume":"87","author":"Roy","year":"2017","journal-title":"Proc. Natl. Acad. Sci. India Sect. A Phys. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","first-page":"595","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"9","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"732","DOI":"10.12677\/AG.2017.76074","article-title":"Comparative study on the water index of MNDWI and NDWI for water boundary extraction in eutrophic lakes","volume":"7","author":"Wang","year":"2017","journal-title":"Adv. Geosci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deoli, V., Kumar, D., and Kuriqi, A. (2022). Detection of water spread area changes in eutrophic lake using landsat data. Sensors, 22.","DOI":"10.3390\/s22186827"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tran, K.H., Menenti, M., and Jia, L. (2022). Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold. Remote Sens., 14.","DOI":"10.3390\/rs14225721"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.jtusci.2016.04.005","article-title":"Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey","volume":"11","author":"Sarp","year":"2017","journal-title":"J. Taibah Univ. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nhu, V.H., Shahabi, H., Nohani, E., Shirzadi, A., Al-Ansari, N., Bahrami, S., Miraki, S., Geertsema, M., and Nguyen, H. (2020). Daily water level prediction of Zrebar Lake (Iran): A comparison between M5P, random forest, random tree and reduced error pruning trees algorithms. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080479"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1750001","DOI":"10.1142\/S1469026817500018","article-title":"Convolutional neural networks for water body extraction from Landsat imagery","volume":"16","author":"Yu","year":"2017","journal-title":"Int. J. Comput. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Gao, X., Zhang, Y., and Zhao, G. (2020). MSLWENet: A novel deep learning network for lake water body extraction of Google remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12244140"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","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":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108258","DOI":"10.1016\/j.ecolind.2021.108258","article-title":"Using the Google Earth Engine to rapidly monitor impacts of geohazards on ecological quality in highly susceptible areas","volume":"132","author":"Yan","year":"2021","journal-title":"Ecol Indic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rama.2023.10.007","article-title":"Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification","volume":"92","author":"Zhao","year":"2024","journal-title":"Rangeland Ecol. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1186\/1475-2875-13-421","article-title":"Fine-scale malaria risk mapping from routine aggregated case data","volume":"13","author":"Sturrock","year":"2014","journal-title":"Malar. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, J., Xiao, X., Wang, X., Dai, S., and Zhao, B. (2019). Long-term dynamic of Poyang Lake surface water: A mapping work based on the Google Earth Engine cloud platform. Remote Sens., 11.","DOI":"10.3390\/rs11030313"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"934033","DOI":"10.3389\/feart.2022.934033","article-title":"A Quick Detection of Lake Area Changes and Hazard Assessment in the Qinghai\u2013Tibet Plateau Based on GEE: A Case Study of Tuosu Lake","volume":"10","author":"Sha","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, J., Peng, B., Wei, Y., and Ye, H. (2021). Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253209"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4002","DOI":"10.1109\/JSTARS.2017.2705718","article-title":"Extraction of glacial lake outlines in Tibet Plateau using Landsat 8 imagery and Google Earth Engine","volume":"10","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9821275","DOI":"10.34133\/2022\/9821275","article-title":"Glacial Lake area changes in high mountain asia during 1990\u20132020 using satellite remote sensing","volume":"2022","author":"Zhang","year":"2022","journal-title":"Research"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/JSTARS.2020.2971783","article-title":"An urban water extraction method combining deep learning and Google Earth engine","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100005","DOI":"10.1016\/j.ophoto.2021.100005","article-title":"Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine","volume":"2","author":"Mayer","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","first-page":"102928","article-title":"Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1038\/s43017-022-00317-5","article-title":"Plant phenology changes and drivers on the Qinghai\u2013Tibetan Plateau","volume":"3","author":"Shen","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103269","DOI":"10.1016\/j.earscirev.2020.103269","article-title":"Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms","volume":"208","author":"Zhang","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_35","unstructured":"Zhang, Y., Ren, H., and Pan, X. (2019). Integration Dataset of Tibet Plateau Boundary."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"146253","DOI":"10.1016\/j.scitotenv.2021.146253","article-title":"An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran","volume":"778","author":"Garajeh","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","article-title":"Hyperspectral image reconstruction by deep convolutional neural network for classification","volume":"63","author":"Li","year":"2017","journal-title":"Pattern Recogn."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recogn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6554","DOI":"10.1080\/01431161.2017.1362131","article-title":"A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification","volume":"38","author":"Pan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","unstructured":"Zhang, G. (2019). The Lakes Larger than 1 km2 in Tibetan Plateau (v3.1) (1970s\u20132022)."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land cover classification using Google Earth Engine and random forest classifier\u2014The role of image composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, Near real-time global 10 m land use land cover mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 12\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:54:23Z","timestamp":1760104463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,3]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030583"],"URL":"https:\/\/doi.org\/10.3390\/rs16030583","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,3]]}}}