{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:44:56Z","timestamp":1782575096280,"version":"3.54.5"},"reference-count":79,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071085"],"award-info":[{"award-number":["42071085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project of the State Key Laboratory of Cryospheric Science","award":["SKLCS 2020-10"],"award-info":[{"award-number":["SKLCS 2020-10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)\/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.<\/jats:p>","DOI":"10.3390\/rs13132595","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T10:06:34Z","timestamp":1625220394000},"page":"2595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-5270","authenticated-orcid":false,"given":"Yijie","family":"Lu","sequence":"first","affiliation":[{"name":"School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China"},{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2160-8619","authenticated-orcid":false,"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China"},{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-0722","authenticated-orcid":false,"given":"Donghui","family":"Shangguan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhua","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2013.07.043","article-title":"The Glaciers Climate Change Initiative: Methods for Creating Glacier Area, Elevation Change and Velocity Products","volume":"162","author":"Paul","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s40333-020-0061-2","article-title":"Glacier Variations and Their Response to Climate Change in an Arid Inland River Basin of Northwest China","volume":"12","author":"Zhou","year":"2020","journal-title":"J. Arid Land"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1017\/jog.2016.137","article-title":"Glacier Changes on the Tibetan Plateau Derived from Landsat Imagery: Mid-1970s\u20132000\u201313","volume":"63","author":"Ye","year":"2017","journal-title":"J. Glaciol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1002\/esp.5042","article-title":"Surface Velocity Fields of Active Rock Glaciers and Ice-debris Complexes in the Central Andes of Argentina","volume":"46","author":"Halla","year":"2021","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jhydrol.2019.03.043","article-title":"Projecting Climate Change Impacts on Hydrological Processes on the Tibetan Plateau with Model Calibration against the Glacier Inventory Data and Observed Streamflow","volume":"573","author":"Zhao","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_6","unstructured":"Regine, H., Rasul, G., Adler, C., C\u00e1ceres, B., Gruber, S., Hirabayashi, Y., and Jackson, M. (2019). High Mountain Areas. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1126\/science.1183188","article-title":"Climate Change Will Affect the Asian Water Towers","volume":"328","author":"Immerzeel","year":"2010","journal-title":"Science"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s43017-020-00124-w","article-title":"Glacial Change and Hydrological Implications in the Himalaya and Karakoram","volume":"2","author":"Nie","year":"2021","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_10","first-page":"343","article-title":"Changes in the Global Cryosphere and Their Impacts: A Review and New Perspective","volume":"12","author":"Liu","year":"2020","journal-title":"Sci. Cold Arid. Reg."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhang, Z., and Huang, D. (2020). Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir. Water, 12.","DOI":"10.3390\/w12113231"},{"key":"ref_12","first-page":"3","article-title":"The contemporary glaciers in China based on the Second Chinese Glacier Inventory","volume":"70","author":"Liu","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.gloplacha.2014.08.006","article-title":"Glacier Volume and Area Change by 2050 in High Mountain Asia","volume":"122","author":"Zhao","year":"2014","journal-title":"Glob. Planet. Chang."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, S., Jiang, Z., Shangguan, D., Wei, J., Guo, W., Xu, J., Zhang, Y., Zhang, S., and Huang, D. (2020). Glacier Variations at Xinqingfeng and Malan Ice Caps in the Inner Tibetan Plateau Since 1970. Remote Sens., 12.","DOI":"10.3390\/rs12030421"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1029\/2018JF004838","article-title":"Heterogeneous Influence of Glacier Morphology on the Mass Balance Variability in High Mountain Asia","volume":"124","author":"Brun","year":"2019","journal-title":"J. Geophys. Res. Earth"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huo, D., Chi, Z., and Ma, A. (2021). Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia. Water, 13.","DOI":"10.3390\/w13010101"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103212","DOI":"10.1016\/j.earscirev.2020.103212","article-title":"Hydrology of Debris-Covered Glaciers in High Mountain Asia","volume":"207","author":"Miles","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1017\/jog.2020.98","article-title":"Spatiotemporal Variability of Surface Velocities of Monsoon Temperate Glaciers in the Kangri Karpo Mountains, Southeastern Tibetan Plateau","volume":"67","author":"Wu","year":"2021","journal-title":"J. Glaciol."},{"key":"ref_19","unstructured":"Buchroithner, M.F., and Bolch, T. (2006, January 14\u201322). An Automated Method to Delineate the Ice Extension of the Debris-Covered Glaciers at Mt. Everest Based on ASTER Imagery. Proceedings of the 9th International Symposium on High Mountain Remote Sensing Cartography, Graz, Austria."},{"key":"ref_20","unstructured":"Biddle, D. (2015). Mapping Debris-Covered Glaciers in the Cordillera Blanca, Peru: An Object-Based Image Analysis Approach. [Master\u2019s Thesis, University of Louisville]."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Racoviteanu, A.E., Nicholson, L., and Glasser, N.F. (2021). Surface Composition of Debris-Covered Glaciers across the Himalaya Using Spectral Unmixing and Multi-Sensor Imagery. Cryosphere Discuss., 1\u201348.","DOI":"10.5194\/tc-2020-372"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fleischer, F., Otto, J., Junker, R.R., and H\u00f6lbling, D. (2021). Evolution of Debris Cover on Glaciers of the Eastern Alps, Austria, between 1996 and 2015. Earth Surf. Process. Landf., esp.5065.","DOI":"10.1002\/esp.5065"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"308","DOI":"10.3389\/feart.2020.00308","article-title":"Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990\u223c2019","volume":"8","author":"Xie","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, J., Menenti, M., and Hu, G. (2019). Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study. Remote Sens., 11.","DOI":"10.3390\/rs11040452"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112376","DOI":"10.1016\/j.rse.2021.112376","article-title":"An Automatic Method for Clean Glacier and Nonseasonal Snow Area Change Estimation in High Mountain Asia from 1990 to 2018","volume":"258","author":"Huang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"146492","DOI":"10.1016\/j.scitotenv.2021.146492","article-title":"Integrated Approach for Effective Debris Mapping in Glacierized Regions of Chandra River Basin, Western Himalayas, India","volume":"779","author":"Pandey","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_28","first-page":"601","article-title":"On Drivers of Subpixel Classification Accuracy\u2014An Example from Glacier Facies","volume":"13","author":"Yousuf","year":"2020","journal-title":"IEEE J STARS"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hoeser, T., Bachofer, F., and Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review\u2014Part II: Applications. Remote Sens., 12.","DOI":"10.3390\/rs12183053"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hoeser, T., and Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sens., 12.","DOI":"10.3390\/rs12101667"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2019.03.015","article-title":"A New Fully Convolutional Neural Network for Semantic Segmentation of Polarimetric SAR Imagery in Complex Land Cover Ecosystem","volume":"151","author":"Mohammadimanesh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hoekstra, M., Jiang, M., Clausi, D.A., and Duguay, C. (2020). Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling. Remote Sens., 21.","DOI":"10.3390\/rs12091425"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2020). Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review. Remote Sens., 12.","DOI":"10.3390\/rs12091444"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"112209","DOI":"10.1016\/j.rse.2020.112209","article-title":"Automatic Water Detection from Multidimensional Hierarchical Clustering for Sentinel-2 Images and a Comparison with Level 2A Processors","volume":"253","author":"Cordeiro","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/01431161.2018.1513666","article-title":"Very High Resolution Remote Sensing Image Classification with SEEDS-CNN and Scale Effect Analysis for Superpixel CNN Classification","volume":"40","author":"Lv","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Marochov, M., Stokes, C.R., and Carbonneau, P.E. (2020). Image Classification of Marine-Terminating Outlet Glaciers Using Deep Learning Methods. Cryosphere Discuss., 1\u201345.","DOI":"10.5194\/tc-2020-310"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Prakash, K.B., and Kanagachidambaresan, G.R. (2021). Convolutional Neural Network. Programming with TensorFlow: Solution for Edge Computing Applications, Springer International Publishing. EAI\/Springer Innovations in Communication and Computing.","DOI":"10.1007\/978-3-030-57077-4"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1080\/01431161.2019.1694725","article-title":"Analysis of Various Optimizers on Deep Convolutional Neural Network Model in the Application of Hyperspectral Remote Sensing Image Classification","volume":"41","author":"Bera","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","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|SpringerLink","volume":"46","author":"Nijhawan","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_41","first-page":"C51B-1272","article-title":"Mapping Himalayan and Karakoram Glaciers Using Deep Learning Approach","volume":"2019","author":"Xie","year":"2019","journal-title":"AGU Fall Meet. Abstr."},{"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","first-page":"136794","DOI":"10.1109\/ACCESS.2020.3011587","article-title":"Corrections to GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, W., Chen, X., Ran, J., Liu, L., Wang, Q., Xin, L., and Li, G. (2021). LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13010056"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Petrovska, B., Zdravevski, E., Lameski, P., Corizzo, R., \u0160tajduhar, I., and Lerga, J. (2020). Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification. Sensors, 20.","DOI":"10.3390\/s20143906"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E. (2019). Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study. Remote Sens., 11.","DOI":"10.3390\/rs11010074"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random Forest Classifier for Remote Sensing Classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12725","DOI":"10.1109\/ACCESS.2020.2965768","article-title":"Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","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_52","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2020.08.004","article-title":"Exploring Multiscale Object-Based Convolutional Neural Network (Multi-OCNN) for Remote Sensing Image Classification at High Spatial Resolution","volume":"168","author":"Martins","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, L., Bai, C., and Ming, J. (2021). Current Status and Variation since 1964 of the Glaciers around the Ebi Lake Basin in the Warming Climate. Remote Sens., 13.","DOI":"10.3390\/rs13030497"},{"key":"ref_55","unstructured":"Liu, Q., Liu, X., Shen, T., and Qiu, X. (2020, January 16\u201318). Glacier Area Monitoring Based on Deep Learning and Multi-Sources Data. Proceedings of the 10th International Conference on Computer Engineering and Networks, Xi\u2019an, China."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s11629-014-3172-4","article-title":"Glacier Changes since the Early 1960s, Eastern Pamir, China","volume":"13","author":"Zhang","year":"2016","journal-title":"J. Mt. Sci."},{"key":"ref_57","first-page":"397","article-title":"Altitude Structure Characteristics of the Glaciers in China Based on the Second Chinese Glacier Inventory","volume":"72","author":"Zhang","year":"2017","journal-title":"Acta Geogr. Sin."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"419","DOI":"10.5194\/tc-4-419-2010","article-title":"A Glacier Inventory for the Western Nyainqentanglha Range and the Nam Co Basin, Tibet, and Glacier Changes 1976\u20132009","volume":"4","author":"Bolch","year":"2010","journal-title":"Cryosphere"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1017\/jog.2019.20","article-title":"Glacier Mass Balance over the Central Nyainqentanglha Range during Recent Decades Derived from Remote-Sensing Data","volume":"65","author":"Wu","year":"2019","journal-title":"J. Glaciol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"865","DOI":"10.5194\/tc-9-865-2015","article-title":"Climate Regime of Asian Glaciers Revealed by GAMDAM Glacier Inventory","volume":"9","author":"Sakai","year":"2015","journal-title":"Cryosphere"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1080\/01431169408954345","article-title":"NDVI-Derived Land Cover Classifications at a Global Scale","volume":"15","author":"Defries","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., He, Y., Liu, Q., Li, H., Su, F., Liu, G., and Bridhikitti, A. (2020). Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics. Remote Sens., 12.","DOI":"10.3390\/rs12071163"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.rse.2018.08.020","article-title":"Optimising NDWI Supraglacial Pond Classification on Himalayan Debris-Covered Glaciers","volume":"217","author":"Watson","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_64","first-page":"1494","article-title":"Influence of Different Bandwidths on LAI Estimation Using Vegetation Indices","volume":"13","author":"Liang","year":"2020","journal-title":"IEEE J. STARS"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Singh, D.K., Thakur, P.K., Naithani, B.P., and Kaushik, S. (2020). Quantifying the Sensitivity of Band Ratio Methods for Clean Glacier Ice Mapping. Spat. Inf. Res.","DOI":"10.1007\/s41324-020-00352-8"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Leprince, S., Ayoub, F., Klinger, Y., and Avouac, J.-P. (2007, January 23\u201328). Co-Registration of Optically Sensed Images and Correlation (COSI-Corr): An Operational Methodology for Ground Deformation Measurements. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423207"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1017\/jog.2016.81","article-title":"Characterizing the May 2015 Karayaylak Glacier Surge in the Eastern Pamir Plateau Using Remote Sensing","volume":"62","author":"Shangguan","year":"2016","journal-title":"J. Glaciol."},{"key":"ref_69","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_70","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_71","first-page":"499","article-title":"High-Resolution Remote Sensing Image Semantic Segmentation Based on Semi-Supervised Full Convolution Network Method","volume":"49","author":"Geng","year":"2020","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1109\/LGRS.2019.2890996","article-title":"An Entropy and MRF Model-Based CNN for Large-Scale Landsat Image Classification","volume":"16","author":"Zhao","year":"2019","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1080\/01431161.2020.1809742","article-title":"Spatiotemporal Fusion of Remote Sensing Images Using a Convolutional Neural Network with Attention and Multiscale Mechanisms","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","first-page":"283","article-title":"Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J STARS"},{"key":"ref_75","unstructured":"Kaplan, N.H., and Erer, I. (2019, January 11\u201314). Remote Sensing Image Enhancement via Robust Guided Filtering. Proceedings of the 2019 9th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/2150704X.2020.1731768","article-title":"Adaptive Conditional Random Field Classification Framework Based on Spatial Homogeneity for High-Resolution Remote Sensing Imagery","volume":"11","author":"Zhong","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [3rd ed.].","DOI":"10.1201\/9780429052729"},{"key":"ref_78","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_79","doi-asserted-by":"crossref","unstructured":"Cohen, J., Cohen, P., West, S.G., and Aiken, L.S. (2013). Applied Multiple Regression\/Correlation Analysis for the Behavioral Sciences, Routledge.","DOI":"10.4324\/9780203774441"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2595\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:17Z","timestamp":1760163917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,2]]},"references-count":79,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132595"],"URL":"https:\/\/doi.org\/10.3390\/rs13132595","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,2]]}}}