{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:01:32Z","timestamp":1773273692212,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19070302"],"award-info":[{"award-number":["XDA19070302"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA20060201"],"award-info":[{"award-number":["XDA20060201"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42130516"],"award-info":[{"award-number":["42130516"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019QZKK020102"],"award-info":[{"award-number":["2019QZKK020102"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42171139"],"award-info":[{"award-number":["42171139"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA19070302"],"award-info":[{"award-number":["XDA19070302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA20060201"],"award-info":[{"award-number":["XDA20060201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130516"],"award-info":[{"award-number":["42130516"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QZKK020102"],"award-info":[{"award-number":["2019QZKK020102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171139"],"award-info":[{"award-number":["42171139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["XDA19070302"],"award-info":[{"award-number":["XDA19070302"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["XDA20060201"],"award-info":[{"award-number":["XDA20060201"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["42130516"],"award-info":[{"award-number":["42130516"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["2019QZKK020102"],"award-info":[{"award-number":["2019QZKK020102"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["42171139"],"award-info":[{"award-number":["42171139"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA19070302"],"award-info":[{"award-number":["XDA19070302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA20060201"],"award-info":[{"award-number":["XDA20060201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130516"],"award-info":[{"award-number":["42130516"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QZKK020102"],"award-info":[{"award-number":["2019QZKK020102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171139"],"award-info":[{"award-number":["42171139"]}],"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>Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985\u20132020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of \u00b124 m, for 13 of the reference glaciers. Eight of these glaciers yield R2 &gt; 0.5, and the other five glaciers yield R2 &gt; 0.3 for their respective SCR\u2013AAR relationships. Furthermore, 10 of these glaciers yield R2 &gt; 0.5 and the other three glaciers yield R2 &gt; 0.3 for their respective SLA\u2013ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield better results than those in Western Canada. This algorithm can be applied to large regions, such as HMA, to obtain snow line estimates where manual approaches are exhaustive and\/or unfeasible. Furthermore, new optical data, such as that from Sentinel-2, can be incorporated to further improve the algorithm results.<\/jats:p>","DOI":"10.3390\/rs14102377","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-1252","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"first","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi\u2019an 710127, China"},{"name":"Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-1141","authenticated-orcid":false,"given":"Ninglian","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi\u2019an 710127, China"},{"name":"Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yuwei","family":"Wu","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi\u2019an 710127, China"},{"name":"Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1038\/nclimate1580","article-title":"Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings","volume":"2","author":"Yao","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/nature23878","article-title":"Impact of a global temperature rise of 1.5 degrees Celsius on Asia\u2019s glaciers","volume":"549","author":"Kraaijenbrink","year":"2017","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/s41586-019-1071-0","article-title":"Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016","volume":"568","author":"Zemp","year":"2019","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"252","DOI":"10.3189\/S0022143000027544","article-title":"Proposed definitions for glacier mass budget terms","volume":"4","author":"Meier","year":"1962","journal-title":"J. Glaciol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3189\/172756409787769744","article-title":"Geodetic and direct mass-balance measurements: Comparison and joint analysis","volume":"50","author":"Cogley","year":"2009","journal-title":"Ann. Glaciol."},{"key":"ref_6","unstructured":"Cuffey, K.M., and Paterson, W.S.B. (2010). The Physics of Glaciers, Academic Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.5194\/tc-12-1899-2018","article-title":"Multi-decadal mass balance series of three Kyrgyz glaciers inferred from modelling constrained with repeated snow line observations","volume":"12","author":"Barandun","year":"2018","journal-title":"Cryosphere"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.3189\/2012JoG12J027","article-title":"Can the snowline be used as an indicator of the equilibrium line and mass balance for glaciers in the outer tropics?","volume":"58","author":"Rabatel","year":"2012","journal-title":"J. Glaciol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1017\/jog.2016.113","article-title":"Spatio-temporal changes in glacier-wide mass balance quantified by optical remote sensing on 30 glaciers in the French Alps for the period 1983\u20132014","volume":"62","author":"Rabatel","year":"2016","journal-title":"J. Glaciol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0034-4257(89)90101-6","article-title":"Spectral signature of alpine snow cover from the Landsat Thematic Mapper","volume":"28","author":"Dozier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6754","DOI":"10.1029\/2019WR024935","article-title":"High-resolution snowline delineation from Landsat imagery to infer snow cover controls in a Himalayan catchment","volume":"55","author":"Miles","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Verbyla, D., Hegel, T., Nolin, A.W., Van de Kerk, M., Kurkowski, T.A., and Prugh, L.R. (2017). Remote sensing of 2000\u20132016 alpine spring snowline elevation in dall sheep mountain ranges of Alaska and Western Canada. Remote Sens., 9.","DOI":"10.3390\/rs9111157"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104","DOI":"10.3189\/S0260305500000471","article-title":"Characterization of snow and ice reflectance zones on glaciers using Landsat Thematic Mapper data","volume":"9","author":"Hall","year":"1987","journal-title":"Ann. Glaciol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"220","DOI":"10.3389\/feart.2019.00220","article-title":"An automated approach for estimating snowline altitudes in the Karakoram and eastern Himalaya from remote sensing","volume":"7","author":"Racoviteanu","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1038\/ngeo2180","article-title":"Contribution of light-absorbing impurities in snow to Greenland\u2019s darkening since 2009","volume":"7","author":"Dumont","year":"2014","journal-title":"Nat. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"558","DOI":"10.3189\/172756506781828412","article-title":"Measuring specific surface area of snow by near-infrared photography","volume":"52","author":"Matzl","year":"2006","journal-title":"J. Glaciol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3389\/feart.2020.00149","article-title":"Region-wide annual glacier surface mass balance for the European Alps from 2000 to 2016","volume":"8","author":"Davaze","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dietz, A.J., and Kuenzer, C. (2019). Deriving regional snow line dynamics during the ablation seasons 1984\u20132018 in European Mountains. Remote Sens., 11.","DOI":"10.3390\/rs11080933"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5194\/tc-12-271-2018","article-title":"Monitoring glacier albedo as a proxy to derive summer and annual surface mass balances from optical remote-sensing data","volume":"12","author":"Davaze","year":"2018","journal-title":"Cryosphere"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, Z., Geng, L., Shen, B., Wu, Y., Chen, A., and Wang, N. (2021). Spatiotemporal variability in the glacier snowline altitude across high mountain asia and potential driving factors. Remote Sens., 13.","DOI":"10.3390\/rs13030425"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, Z., Wang, N., Shen, B., Gu, Z., Wu, Y., and Chen, A. (2021). Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China. Remote Sens., 13.","DOI":"10.3390\/rs13234935"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1657\/AAAR00C-13-303","article-title":"Variations in firn line altitude and firn zone area on Qiyi Glacier, Qilian Mountains, over the period of 1990 to 2011","volume":"47","author":"Guo","year":"2015","journal-title":"Arct. Antarct. Alp. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1016\/j.jhydrol.2014.08.064","article-title":"Estimation of regional snowline elevation (RSLE) from MODIS images for seasonally snow covered mountain basins","volume":"519","author":"Holko","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"649","DOI":"10.3189\/2013JoG12J221","article-title":"Identification of snow ablation rate, ELA, AAR and net mass balance using transient snowline variations on two Arctic glaciers","volume":"59","author":"Mernild","year":"2013","journal-title":"J. Glaciol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.cageo.2015.05.005","article-title":"A GIS tool for automatic calculation of glacier equilibrium-line altitudes","volume":"82","author":"Pellitero","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1017\/jog.2021.100","article-title":"Testing the area\u2013altitude balance ratio (AABR) and accumulation\u2013area ratio (AAR) methods of calculating glacier equilibrium-line altitudes","volume":"68","author":"Oien","year":"2022","journal-title":"J. Glaciol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rastner, P., Prinz, R., Notarnicola, C., Nicholson, L., Sailer, R., Schwaizer, G., and Paul, F. (2019). On the automated mapping of snow cover on glaciers and calculation of snow line altitudes from multi-temporal landsat data. Remote Sens., 11.","DOI":"10.3390\/rs11121410"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1175\/JCLI-D-16-0214.1","article-title":"Impact of Eurasian spring snow decrement on East Asian summer precipitation","volume":"30","author":"Zhang","year":"2017","journal-title":"J. Clim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1080\/2150704X.2017.1375611","article-title":"Glacier snowline altitude variations in the Pamirs, Tajikistan, 1998\u20132013: Insights from remote sensing images","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1002\/joc.5204","article-title":"Spatiotemporal variation of snow cover over the Tibetan Plateau based on MODIS snow product, 2001\u20132014","volume":"38","author":"Li","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3389\/feart.2019.00363","article-title":"A systematic, regional assessment of high mountain Asia glacier mass balance","volume":"7","author":"Shean","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1038\/s41561-018-0271-9","article-title":"Twenty-first century glacier slowdown driven by mass loss in High Mountain Asia","volume":"12","author":"Dehecq","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1038\/ngeo2999","article-title":"A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016","volume":"10","author":"Brun","year":"2017","journal-title":"Nat. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bettiol, G.M., Ferreira, M.E., Motta, L.P., Cremon, \u00c9.H., and Sano, E.E. (2021). Conformity of the NASADEM_HGT and ALOS AW3D30 DEM with the Altitude from the Brazilian Geodetic Reference Stations: A Case Study from Brazilian Cerrado. Sensors, 21.","DOI":"10.3390\/s21092935"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.05.024","article-title":"An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions","volume":"214","author":"Zhu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1038\/nclimate3111","article-title":"Earth\u2019s surface water change over the past 30 years","volume":"6","author":"Donchyts","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3189\/2013AoG63A296","article-title":"On the accuracy of glacier outlines derived from remote-sensing data","volume":"54","author":"Paul","year":"2013","journal-title":"Ann. Glaciol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/s41561-020-0615-0","article-title":"The state of rock debris covering Earth\u2019s glaciers","volume":"13","author":"Herreid","year":"2020","journal-title":"Nat. Geosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1017\/jog.2021.47","article-title":"Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia","volume":"67","author":"Tielidze","year":"2021","journal-title":"J. Glaciol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"81","DOI":"10.5194\/tc-12-81-2018","article-title":"The greater caucasus glacier inventory (Russia, Georgia and Azerbaijan)","volume":"12","author":"Tielidze","year":"2018","journal-title":"Cryosphere"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"489","DOI":"10.5194\/tc-16-489-2022","article-title":"Strong acceleration of glacier area loss in the Greater Caucasus between 2000 and 2020","volume":"16","author":"Tielidze","year":"2022","journal-title":"Cryosphere"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2377\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:53Z","timestamp":1760137853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,14]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102377"],"URL":"https:\/\/doi.org\/10.3390\/rs14102377","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,14]]}}}