{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T23:40:48Z","timestamp":1778542848889,"version":"3.51.4"},"reference-count":85,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:00:00Z","timestamp":1650412800000},"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":["41830105"],"award-info":[{"award-number":["41830105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174127"],"award-info":[{"award-number":["72174127"]}],"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>Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41\u201399.57%) and BC suite (55.85\u201399.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams.<\/jats:p>","DOI":"10.3390\/rs14091980","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T03:46:11Z","timestamp":1650512771000},"page":"1980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Potential of Sentinel-1A Data for Identification of Debris-Covered Alpine Glacier Based on Machine Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5892-6314","authenticated-orcid":false,"given":"Guohui","family":"Yao","sequence":"first","affiliation":[{"name":"Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China"},{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-7634","authenticated-orcid":false,"given":"Xiaobing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Geophysical Engineering, Montana Technological University, Butte, MT 59701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0212-4069","authenticated-orcid":false,"given":"Changqing","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lhakpa","family":"Drolma","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Institute of Tibetan Plateau Atmospheric and Environmental Sciences, Tibet Meteorological Bureau, Lhasa 850000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6634-9593","authenticated-orcid":false,"given":"Haidong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.gr.2014.07.002","article-title":"Political risks arising from the impacts of large-scale afforestation on water resources of the Tibetan Plateau","volume":"28","author":"Cao","year":"2015","journal-title":"Gondwana Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nature11324","article-title":"Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas","volume":"488","author":"Berthier","year":"2012","journal-title":"Nature"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1175\/BAMS-D-13-00047.1","article-title":"The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy","volume":"95","author":"Bojinski","year":"2014","journal-title":"Bull. 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