{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:19:00Z","timestamp":1784236740559,"version":"3.55.0"},"reference-count":74,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"German Academic Exchange Service","doi-asserted-by":"publisher","award":["2021\/22 (57552338)."],"award-info":[{"award-number":["2021\/22 (57552338)."]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology","doi-asserted-by":"publisher","award":["DST\/INSPIRE Fellowship\/2017\/IF170680"],"award-info":[{"award-number":["DST\/INSPIRE Fellowship\/2017\/IF170680"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km2 and 1710 km2 of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.<\/jats:p>","DOI":"10.3390\/rs14061352","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"1352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2791-3729","authenticated-orcid":false,"given":"Saurabh","family":"Kaushik","sequence":"first","affiliation":[{"name":"Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India"},{"name":"CSIR\u2014Central Scientific Instrument Organisation, Chandigarh 160030, India"},{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tejpal","family":"Singh","sequence":"additional","affiliation":[{"name":"Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India"},{"name":"CSIR\u2014Central Scientific Instrument Organisation, Chandigarh 160030, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2502-6384","authenticated-orcid":false,"given":"Anshuman","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"School of Geosciences, University of Aberdeen, Meston Building, King\u2019s College, Aberdeen AB24 3UE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-0167","authenticated-orcid":false,"given":"Pawan K.","family":"Joshi","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India"},{"name":"Special Centre for Disaster Research, Jawaharlal Nehru University, New Delhi 110067, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-7136","authenticated-orcid":false,"given":"Andreas J.","family":"Dietz","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eaav7266","DOI":"10.1126\/sciadv.aav7266","article-title":"Acceleration of ice loss across the Himalayas over the past 40 years","volume":"5","author":"Maurer","year":"2019","journal-title":"Sci. Adv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/s41586-019-1822-y","article-title":"Importance and vulnerability of the world\u2019s water towers","volume":"577","author":"Immerzeel","year":"2020","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s00382-013-1719-7","article-title":"Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models","volume":"42","author":"Bliss","year":"2014","journal-title":"Clim. Dyn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1017\/jog.2019.22","article-title":"GlacierMIP\u2013A model intercomparison of global-scale glacier mass-balance models and projections","volume":"65","author":"Hock","year":"2019","journal-title":"J. Glaciol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"815","DOI":"10.5194\/hess-14-815-2010","article-title":"Future high-mountain hydrology: A new parameterization of glacier retreat","volume":"14","author":"Huss","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12025","DOI":"10.1038\/ncomms12025","article-title":"Ecological responses to experimental glacier-runoff reduction in alpine rivers","volume":"7","author":"Andino","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1890\/07-1188.1","article-title":"Accelerating climate change impacts on alpine glacier forefield ecosystems in the European Alps","volume":"18","author":"Cannone","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s11600-019-00262-w","article-title":"Hydro-geochemical analysis of meltwater draining from Bilare Banga glacier, Western Himalaya","volume":"67","author":"Kumar","year":"2019","journal-title":"Acta Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/s12665-019-8687-0","article-title":"Hydro-geochemical characteristics of glacial meltwater from Naradu Glacier catchment, Western Himalaya","volume":"78","author":"Kumar","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1073\/pnas.97.4.1406","article-title":"Twentieth century climate change: Evidence from small glaciers","volume":"97","author":"Dyurgerov","year":"2000","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s41558-018-0093-1","article-title":"Limited influence of climate change mitigation on short-term glacier mass loss","volume":"8","author":"Marzeion","year":"2018","journal-title":"Nat. Clim. Change"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1080\/15481603.2021.1930730","article-title":"Regional mass variations and its sensitivity to climate drivers over glaciers of Karakoram and Himalayas","volume":"58","author":"Patel","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/s41467-021-23073-4","article-title":"Health and sustainability of glaciers in High Mountain Asia","volume":"12","author":"Miles","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.5194\/tc-13-2977-2019","article-title":"Recent glacier and lake changes in High Mountain Asia and their relation to precipitation changes","volume":"13","author":"Treichler","year":"2019","journal-title":"Cryosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"eaba3645","DOI":"10.1126\/sciadv.aba3645","article-title":"Seismic observations, numerical modeling, and geomorphic analysis of a glacier lake outburst flood in the Himalayas","volume":"6","author":"Maurer","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"136455","DOI":"10.1016\/j.scitotenv.2019.136455","article-title":"Examining the glacial lake dynamics in a warming climate and GLOF modelling in parts of Chandra basin, Himachal Pradesh, India","volume":"714","author":"Kaushik","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1146\/annurev.energy.31.041105.095552","article-title":"Earth\u2019s cryosphere: Current state and recent changes","volume":"31","author":"Parkinson","year":"2006","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_20","first-page":"344","article-title":"State of the Earth\u2019s cryosphere at the beginning of the 21st century: Glaciers, global snow cover, floating ice, and permafrost and periglacial environments","volume":"508","author":"Williams","year":"2012","journal-title":"Director"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2020WR029266","DOI":"10.1029\/2020WR029266","article-title":"Variable 21st century climate change response for rivers in High Mountain Asia at seasonal to decadal time scales","volume":"57","author":"Khanal","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.5194\/tc-7-1263-2013","article-title":"Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999\u20132011","volume":"7","author":"Gardelle","year":"2013","journal-title":"Cryosphere"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"F04010","DOI":"10.1029\/2012JF002523","article-title":"Distributed ice thickness and volume of all glaciers around the globe","volume":"117","author":"Huss","year":"2012","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.coldregions.2014.07.006","article-title":"Mapping debris-covered glaciers and identifying factors affecting the accuracy","volume":"106","author":"Bhardwaj","year":"2014","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6607","DOI":"10.1080\/01431161.2019.1582114","article-title":"Development of glacier mapping in Indian Himalaya: A review of approaches","volume":"40","author":"Kaushik","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2020GL091311","DOI":"10.1029\/2020GL091311","article-title":"Distributed global debris thickness estimates reveal debris significantly impacts glacier mass balance","volume":"48","author":"Rounce","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/ngeo1068","article-title":"Spatially variable response of Himalayan glaciers to climate change affected by debris cover","volume":"4","author":"Scherler","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12843","DOI":"10.1038\/s41598-018-31310-y","article-title":"Heterogeneity in topographic control on velocities of Western Himalayan glaciers","volume":"8","author":"Sam","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1177\/0309133315593894","article-title":"Remote sensing flow velocity of debris-covered glaciers using Landsat 8 data","volume":"40","author":"Sam","year":"2016","journal-title":"Prog. Phys. Geogr."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kaushik, S., Singh, T., Bhardwaj, A., and Joshi, P. (2022). Long-term spatiotemporal variability in the glacier surface velocity of Eastern Himalayan glaciers, India. Earth Surf. Processes Landf.","DOI":"10.1002\/esp.5342"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.gloplacha.2018.05.006","article-title":"Contrasting geometric and dynamic evolution of lake and land-terminating glaciers in the central Himalaya","volume":"167","author":"King","year":"2018","journal-title":"Glob. Planet. Change"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.rse.2010.01.015","article-title":"Synergistic approach for mapping debris-covered glaciers using optical\u2013thermal remote sensing data with inputs from geomorphometric parameters","volume":"114","author":"Shukla","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1029\/91EO00104","article-title":"Transactions American Geophysical Union. Glacier recession in Iceland and Austria","volume":"73","author":"Hall","year":"1992","journal-title":"EoS Trans. Am. Geophys. Union"},{"key":"ref_34","first-page":"237","article-title":"Observed changes in Himalayan glaciers","volume":"106","author":"Kulkarni","year":"2014","journal-title":"Curr. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.rse.2003.11.007","article-title":"Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers","volume":"89","author":"Paul","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.rse.2015.10.001","article-title":"Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment","volume":"170","author":"Robson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.3390\/s8053355","article-title":"Optical remote sensing of glacier characteristics: A review with focus on the Himalaya","volume":"8","author":"Racoviteanu","year":"2008","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107365","DOI":"10.1016\/j.geomorph.2020.107365","article-title":"Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1\/-2 (SAR\/optical), Landsat 8 (thermal) and digital elevation data","volume":"369","author":"Alifu","year":"2020","journal-title":"Geomorphology"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1080\/10106049.2018.1557260","article-title":"Climate change drives glacier retreat in Bhaga basin located in Himachal Pradesh, India","volume":"35","author":"Kaushik","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8095","DOI":"10.1080\/01431161.2010.532821","article-title":"Mapping of debris-covered glaciers in the Garhwal Himalayas using ASTER DEMs and thermal data","volume":"32","author":"Bhambri","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"83495","DOI":"10.1109\/ACCESS.2020.2991187","article-title":"GlacierNet: A deep-learning approach for debris-covered glacier mapping","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhang, Z., Shangguan, D., and Yang, J. (2021). Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers. Remote Sens., 13.","DOI":"10.3390\/rs13132595"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/10106049908542100","article-title":"SPOT panchromatic imagery and neural networks for information extraction in a complex mountain environment","volume":"14","author":"Bishop","year":"1999","journal-title":"Geocarto Int."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s10584-008-9393-1","article-title":"Sensitivity of European glaciers to precipitation and temperature\u2014Two case studies","volume":"90","author":"Steiner","year":"2008","journal-title":"Clim. Change"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112033","DOI":"10.1016\/j.rse.2020.112033","article-title":"Automated detection of rock glaciers using deep learning and object-based image analysis","volume":"250","author":"Robson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dirscherl, M., Dietz, A.J., Kneisel, C., and Kuenzer, C. (2021). A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning. Remote Sens., 13.","DOI":"10.5194\/egusphere-egu21-508"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s12524-018-0750-x","article-title":"A hybrid CNN+ random forest approach to delineate debris covered glaciers using deep features","volume":"46","author":"Nijhawan","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Baumhoer, C.A., Dietz, A.J., Kneisel, C., and Kuenzer, C. (2019). Automated extraction of antarctic glacier and ice shelf fronts from sentinel-1 imagery using deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11212529"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5041","DOI":"10.5194\/tc-15-5041-2021","article-title":"Image Classification of Marine-Terminating Outlet Glaciers using Deep Learning Methods","volume":"15","author":"Marochov","year":"2020","journal-title":"Cryosphere"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.5194\/essd-12-1973-2020","article-title":"A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967\u20132015","volume":"12","author":"Bolibar","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1073\/pnas.1914898117","article-title":"Hazard from Himalayan glacier lake outburst floods","volume":"117","author":"Veh","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.scitotenv.2019.07.086","article-title":"On the strongly imbalanced state of glaciers in the Sikkim, eastern Himalaya, India","volume":"691","author":"Garg","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/s41558-021-01028-3","article-title":"Increasing risk of glacial lake outburst floods from future Third Pole deglaciation","volume":"11","author":"Zheng","year":"2021","journal-title":"Nat. Clim. Change"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41561-019-0513-5","article-title":"Manifestations and mechanisms of the Karakoram glacier Anomaly","volume":"13","author":"Farinotti","year":"2020","journal-title":"Nat. Geosci."},{"key":"ref_57","first-page":"38","article-title":"Karakoram glacier surge dynamics","volume":"28","author":"Quincey","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.11.008","article-title":"A regional-scale assessment of Himalayan glacial lake changes using satellite observations from 1990 to 2015","volume":"189","author":"Nie","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2017.07.017","article-title":"A patch-based convolutional neural network for remote sensing image classification","volume":"95","author":"Sharma","year":"2017","journal-title":"Neural Netw."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.3390\/rs4103078","article-title":"Decision tree and texture analysis for mapping debris-covered glaciers in the Kangchenjunga area, Eastern Himalaya","volume":"4","author":"Racoviteanu","year":"2012","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1017\/jog.2018.70","article-title":"Automatic delineation of debris-covered glaciers using InSAR coherence derived from X-, C-and L-band radar data: A case study of Yazgyl Glacier","volume":"64","author":"Lippl","year":"2018","journal-title":"J. Glaciol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"S186","DOI":"10.5589\/m10-014","article-title":"Using L-band SAR coherence to delineate glacier extent","volume":"36","author":"Atwood","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.5194\/essd-13-2923-2021","article-title":"Glacier Changes in the Chhombo Chhu Watershed of Tista basin between 1975 and 2018, Sikkim Himalaya, India","volume":"13","author":"Chowdhury","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.3189\/2013JoG12J184","article-title":"The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya, India","volume":"59","author":"Basnett","year":"2013","journal-title":"J. Glaciol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3546062","DOI":"10.1029\/2009JF001426","article-title":"Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge","volume":"115","author":"Bookhagen","year":"2010","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.rse.2012.06.020","article-title":"Compilation of a glacier inventory for the western Himalayas from satellite data: Methods, challenges, and results","volume":"124","author":"Frey","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3189\/2014JoG13J176","article-title":"The Randolph Glacier Inventory: A globally complete inventory of glaciers","volume":"60","author":"Pfeffer","year":"2014","journal-title":"J. Glaciol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zlateski, A., Jaroensri, R., Sharma, P., and Durand, F. (2018, January 18\u201322). On the importance of label quality for semantic segmentation. In Proceedings the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00160"},{"key":"ref_70","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1080\/07038992.2020.1805729","article-title":"A Comprehensive Survey of Optical Remote Sensing Image Segmentation Methods","volume":"46","author":"Wang","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_74","first-page":"499","article-title":"High-resolution remote sensing image semantic segmentation based on semi-supervised full convolution network method","volume":"49","author":"Yanlei","year":"2020","journal-title":"Acta Geod. Cartogr. Sin."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1352\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:34:37Z","timestamp":1760135677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":74,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061352"],"URL":"https:\/\/doi.org\/10.3390\/rs14061352","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,10]]}}}