{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:37:16Z","timestamp":1769510236671,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:00:00Z","timestamp":1718496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Funds","award":["62001451"],"award-info":[{"award-number":["62001451"]}]},{"name":"National Natural Science Funds","award":["D010206"],"award-info":[{"award-number":["D010206"]}]},{"name":"Civil Space Pre-Research Project","award":["62001451"],"award-info":[{"award-number":["62001451"]}]},{"name":"Civil Space Pre-Research Project","award":["D010206"],"award-info":[{"award-number":["D010206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial\u2013temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg\u2013Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments.<\/jats:p>","DOI":"10.3390\/rs16122187","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T04:48:12Z","timestamp":1718599692000},"page":"2187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4851-706X","authenticated-orcid":false,"given":"Lu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6800-492X","authenticated-orcid":false,"given":"Jili","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3635-5826","authenticated-orcid":false,"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-1550","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8756-2211","authenticated-orcid":false,"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1282-1374","authenticated-orcid":false,"given":"Yuanzhao","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,16]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"Slope movement types and processes","volume":"176","author":"Varnes","year":"1978","journal-title":"Transp. Res. Board Spec. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Luo, S., Feng, G., Xiong, Z., Wang, H., Zhao, Y., Li, K., Deng, K., and Wang, Y. (2021). An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements. Remote Sens., 13.","DOI":"10.3390\/rs13173490"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ran, P., Li, S., Zhuo, G., Wang, X., Meng, M., Liu, L., Chen, Y., Huang, H., Ye, Y., and Lei, X. (2023). Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS-InSAR. Sustainability, 15.","DOI":"10.3390\/su15054366"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, K., Xu, P., Cao, C., Zheng, L., Liu, Y., and Dong, X. (2021). Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods. Sustainability, 13.","DOI":"10.3390\/su13031017"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.5194\/nhess-10-1865-2010","article-title":"Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study","volume":"10","author":"Notti","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ren, T., Gong, W., Bowa, V., Tang, H., Chen, J., and Zhao, F. (2021). An Improved R-Index Model for Terrain Visibility Analysis for Landslide Monitoring with InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13101938"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3856","DOI":"10.1109\/TGRS.2011.2139217","article-title":"Accounting for Atmospheric Delays in InSAR Data in a Search for Long-Wavelength Deformation in South America","volume":"49","author":"Fournier","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Su, Y., Yang, H., Peng, J., Liu, Y., Zhao, B., and Shi, M. (2022). A Novel Near-Real-Time GB-InSAR Slope Deformation Monitoring Method. Remote Sens., 14.","DOI":"10.3390\/rs14215585"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.isprsjprs.2013.03.008","article-title":"Improved topographic mapping through high-resolution SAR interferometry with atmospheric effect removal","volume":"80","author":"Liao","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_12","first-page":"5209115","article-title":"Reduction of Atmospheric Effects on InSAR Observations through Incorporation of GACOS and PCA Into Small Baseline Subset InSAR","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zuo, X., Li, Y., Guo, S., Bu, J., and Yang, Q. (2023). Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region. Remote Sens., 15.","DOI":"10.3390\/rs15040990"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5952","DOI":"10.1002\/2015JB011886","article-title":"Mitigation of atmospheric phase delays in InSAR data, with application to the eastern California shear zone","volume":"120","author":"Tymofyeyeva","year":"2015","journal-title":"J. Geophys. Res. Solid Earth."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/JSTARS.2020.2969726","article-title":"Performance Evaluation of Phase and Weather-Based Models in Atmospheric Correction with Sentinel-1 Data: Corvara Landslide in the Alps","volume":"13","author":"Darvishi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, H., Zhang, L., Lu, Z., and Li, X. (2023). Correction of spatially varying stratified atmospheric delays in multitemporal InSAR. Remote Sens. Environ., 285.","DOI":"10.1016\/j.rse.2022.113382"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zeng, Q., and Zhang, Z. (2023). Evaluation of InSAR Tropospheric Correction by Using Efficient WRF Simulation with ERA5 for Initialization. Remote Sens., 15.","DOI":"10.3390\/rs15010273"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Owerko, T., Kuras, P., and Ortyl, \u0141. (2020). Atmospheric Correction Thresholds for Ground-Based Radar Interferometry Deformation Monitoring Estimated Using Time Series Analyses. Remote Sens., 12.","DOI":"10.3390\/rs12142236"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2017.10.038","article-title":"Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model","volume":"204","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"102289","article-title":"Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China","volume":"96","author":"Xiao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","first-page":"102439","article-title":"Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India","volume":"102","author":"Karanam","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","first-page":"B09102","article-title":"Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network","volume":"111","author":"Onn","year":"2006","journal-title":"Geophys. Res. Solid Earth."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1002\/2014JB011558","article-title":"A spatially variable power law tropospheric correction technique for InSAR data","volume":"120","author":"Bekaert","year":"2015","journal-title":"Geophys. Res. Solid Earth."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112097","DOI":"10.1016\/j.rse.2020.112097","article-title":"Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikura earthquake from InSAR time series with atmospheric corrections","volume":"251","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5231414","DOI":"10.1109\/TGRS.2022.3188988","article-title":"Development of InSAR Neutral Atmospheric Delay Correction Model by Use of GNSS ZTD and Its Horizontal Gradient","volume":"60","author":"Kinoshita","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TGRS.2018.2853706","article-title":"Toward Mitigating Stratified Tropospheric Delays in Multitemporal InSAR: A Quadtree Aided Joint Model","volume":"57","author":"Liang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113013","DOI":"10.1016\/j.rse.2022.113013","article-title":"Refined InSAR tropospheric delay correction for wide-area landslide identification and monitoring","volume":"275","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10509","DOI":"10.1109\/JSTARS.2021.3113619","article-title":"An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas","volume":"14","author":"Shi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1002\/ggge.20258","article-title":"Characterizing and estimating noise in InSAR and InSAR time series with MODIS","volume":"14","author":"Barnhart","year":"2013","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.rse.2015.08.035","article-title":"Statistical comparison of InSAR tropospheric correction techniques","volume":"170","author":"Bekaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"B03403","article-title":"Ground motion measurement in the Lake Mead area, Nevada, by differential synthetic aperture radar interferometry time series analysis: Probing the lithosphere rheological structure","volume":"112","author":"Doin","year":"2007","journal-title":"J. Geophys. Res. Solid Earth."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1029\/98GL02112","article-title":"Tropospheric corrections of SAR interferograms with strong topography. Application to Etna","volume":"25","author":"Delacourt","year":"1998","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1029\/2010GC003228","article-title":"A multiscale approach to estimating topographically correlated propagation delays in radar interferograms","volume":"11","author":"Lin","year":"2010","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.earscirev.2019.03.008","article-title":"Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating","volume":"192","author":"Li","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/01431161.2010.536185","article-title":"Persistent scatterers interferometry hotspot and cluster analysis (PSI-HCA) for detection of extremely slow-moving landslides","volume":"33","author":"Lu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dai, H., Zhang, H., Dai, H., Wang, C., Tang, W., Zou, L., and Tang, Y. (2022). Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14184504"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ni, W., Zhao, L., Zhang, L., Xing, K., and Dou, J. (2023). Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China. Remote Sens., 15.","DOI":"10.3390\/rs15092296"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107590","DOI":"10.1016\/j.soildyn.2022.107590","article-title":"Site response of ancient landslides to initial impoundment of Baihetan Reservoir (China) based on ambient noise investigation","volume":"167","author":"Liu","year":"2023","journal-title":"Soil Dyn. Eearthq. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1007\/s10346-023-02093-9","article-title":"Deformation behavior and triggering mechanism of the Tuandigou landslide around the reservoir area of Baihetan hydropower station","volume":"20","author":"Cheng","year":"2023","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s10346-022-02010-6","article-title":"Deformation characteristics, mechanisms, and potential impulse wave assessment of the Wulipo landslide in the Baihetan reservoir region, China","volume":"20","author":"Yi","year":"2023","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1253272","DOI":"10.3389\/feart.2023.1253272","article-title":"InSAR-based method for deformation monitoring of landslide source area in Baihetan reservoir, China","volume":"11","author":"Liu","year":"2023","journal-title":"Front. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dun, J., Feng, W., Yi, X., Zhang, G., and Wu, M. (2021). Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method. Remote Sens., 13.","DOI":"10.3390\/rs13163213"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5755","DOI":"10.1007\/s13369-020-05128-8","article-title":"Accuracy assessment of DEMs derived from multiple SAR data using the InSAR technique","volume":"46","author":"Karabork","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, T., Tang, X., Zhang, X., Fan, H., and Wang, Y. (2022). Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield. Remote Sens., 14.","DOI":"10.3390\/rs14143314"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/17538947.2015.1116624","article-title":"Measurement of subsidence in the Yangbajing geothermal fields, Tibet, from TerraSAR-X InSAR time series analysis","volume":"9","author":"Li","year":"2016","journal-title":"Int. J. Digit. Earth."},{"key":"ref_46","first-page":"103082","article-title":"InSAR stacking with atmospheric correction for rapid geohazard detection: Applications to ground subsidence and landslides in China","volume":"115","author":"Xiao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jia, H., Wang, Y., Ge, D., Deng, Y., and Wang, R. (2022). InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation-A Case of Xiaojiang River Basin, China. Remote Sens., 14.","DOI":"10.3390\/rs14071759"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4008705","DOI":"10.1109\/LGRS.2024.3390568","article-title":"Automated Reference Points Selection for InSAR Time Series Analysis on Segmented Wetlands","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111983","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","first-page":"5232914","article-title":"Predictable Condition Analysis and Prediction Method of SBAS-InSAR Coal Mining Subsidence","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111750","DOI":"10.1016\/j.rse.2020.111750","article-title":"Characterizing marsh wetlands in the Great Lakes Basin with C-band InSAR observations","volume":"242","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, Z., and Mallorqu\u00ed, J. (2019). An Accurate Method to Correct Atmospheric Phase Delay for InSAR with the ERA5 Global Atmospheric Model. Remote Sens., 11.","DOI":"10.3390\/rs11171969"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9202","DOI":"10.1029\/2017JB015305","article-title":"Generic atmospheric correction model for interferometric synthetic aperture radar observations","volume":"123","author":"Yu","year":"2018","journal-title":"J. Geophys. Res. Solid Earth"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:37Z","timestamp":1760108377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,16]]},"references-count":53,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122187"],"URL":"https:\/\/doi.org\/10.3390\/rs16122187","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,16]]}}}