{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T01:27:08Z","timestamp":1770686828350,"version":"3.49.0"},"reference-count":78,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Academy of Sciences  the Strategic Priority Research Program","award":["XDA19090126"],"award-info":[{"award-number":["XDA19090126"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41930110"],"award-info":[{"award-number":["41930110"]}],"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>The Qinghai-Tibet Railway (QTR) is the railway with the highest elevation and longest distance in the world, spanning more than 1142 km from Golmud to Lhasa across the continuous permafrost region. Due to climate change and anthropogenic activities, geological disasters such as subsidence and thermal melt collapse have occurred in the QTR embankment. To conduct the large-scale permafrost monitoring and geohazard investigation along the QTR, we collected 585 Sentinel-1A images based on the composite index model using the multitrack time-series interferometry synthetic aperture radar (MTS-InSAR) method to retrieve the surface deformation over a 3.15 \u00d7 105 km2 area along the QTR. Meanwhile, a new method for permafrost distribution mapping based on InSAR time series deformation was proposed. Finally, the seasonal deformation map and a new map of permafrost distribution along the QTR from Golmud to Lhasa were obtained. The results showed that the estimated seasonal deformation range of the 10 km buffer zone along the QTR was \u221250\u201310 mm, and the LOS deformation rate ranged from \u221230 to 15 mm\/yr. In addition, the deformation results were validated by leveling measurements, and the range of absolute error was between 0.1 and 4.62 mm. Most of the QTR was relatively stable. Some geohazard-prone sections were detected and analyzed along the QTR. The permafrost distribution results were mostly consistent with the simulated results of Zou\u2019s method, based on the temperature at the top of permafrost (TTOP) model. This study reveals recent deformation characteristics of the QTR, and has significant scientific implications and applicational value for ensuring the safe operation of the QTR. Moreover, our method, based on InSAR results, provides new insights for permafrost classification on the Qinghai-Tibet Plateau (QTP).<\/jats:p>","DOI":"10.3390\/rs13234744","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Freeze-Thaw Deformation Cycles and Temporal-Spatial Distribution of Permafrost along the Qinghai-Tibet Railway Using Multitrack InSAR Processing"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4887-923X","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9847-898X","authenticated-orcid":false,"given":"Yixian","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Wei","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0123-6993","authenticated-orcid":false,"given":"Longkai","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dobi\u0144ski, W. 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