{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:34:08Z","timestamp":1768692848305,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071429"],"award-info":[{"award-number":["42071429"]}]},{"name":"National Natural Science Foundation of China","award":["KF-2023-08-19"],"award-info":[{"award-number":["KF-2023-08-19"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["42071429"],"award-info":[{"award-number":["42071429"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2023-08-19"],"award-info":[{"award-number":["KF-2023-08-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.<\/jats:p>","DOI":"10.3390\/rs16162873","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T15:24:16Z","timestamp":1722957856000},"page":"2873","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Optimized Landslide Susceptibility Mapping and Modelling Using the SBAS-InSAR Coupling Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-472X","authenticated-orcid":false,"given":"Xueling","family":"Wu","sequence":"first","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China"}]},{"given":"Xiaoshuai","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6951-8980","authenticated-orcid":false,"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Junyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.catena.2019.03.005","article-title":"Monitoring of remedial works performance on landslide-affected areas through ground-and satellite-based techniques","volume":"178","author":"Confuorto","year":"2019","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40677-018-0104-6","article-title":"Direct impacts of landslides on socio-economic systems: A case study from Aranayake, Sri Lanka","volume":"5","author":"Perera","year":"2018","journal-title":"Geoenviron. 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