Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement

Chengyan Fan 1, 2
Cuicui Mu 1, 3
Lin Liu 2
Zhang Tingjun 1
Shichao Jia 1
Shengdi Wang 1
Sun Wen 1
Zhuoyi Zhao 2
Publication typeJournal Article
Publication date2025-04-01
scimago Q1
wos Q1
SJR3.480
CiteScore19.6
Impact factor12.2
ISSN09242716, 18728235
Abstract
InSAR is an effective tool for indirectly monitoring large-scale hydrological-thermal dynamics of the active layer and permafrost by detecting the surface deformation. However, the conventional time-series models of InSAR technology do not consider the distinctive and pronounced seasonal characteristics of deformation over permafrost. Although permafrost-tailored models have been developed, their performance relative to the conventional models has not been assessed. In this study, we modify sinusoidal function and Stefan-equation-based models (permafrost-tailored) to better characterize surface deformation over permafrost, and assess advantages and limitations of these models for three application scenarios: filling time-series gaps for Small Baseline Subset (SBAS) inversion, deriving velocity and amplitude of deformation and selecting reference points automatically. The HyP3 interferograms generated from Sentinel-1 are utilized to analyze the surface deformation of the permafrost region over the upper reaches of the Heihe River Basin from 2017 to 2023. The result shows that adding a semi-annual component to the sinusoidal function can better capture the characteristics of ground surface deformation in permafrost regions. The modified Stefan-equation-based model performs well in those application scenarios, but it is only recommended for complex scenarios that conventional mathematical models cannot handle or for detailed simulations at individual points due to sophisticated data preparation and high computational cost. Furthermore, we find reference points can introduce substantial uncertainties into the deformation velocity and amplitude measurements, in comparison to the uncertainties derived from interferograms alone. The analysis of deformation amplitude and inter-annual velocity reveals that an ice-rich permafrost region, exhibiting a seasonal amplitude of 50–130 mm, is experiencing rapid degradation characterized by a subsidence velocity ranging from −10 to −20 mm/yr. Our study gives a permafrost-tailored modification and quantitative assessment on the InSAR time-series models. It can also serve as a reference and promotion for the application of InSAR technology in future permafrost research. The dataset and code are available at https://github.com/Fanchengyan/FanInSAR.
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Fan C. et al. Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement // ISPRS Journal of Photogrammetry and Remote Sensing. 2025. Vol. 222. pp. 167-185.
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Fan C., Mu C., Liu L., Tingjun Z., Jia S., Wang S., Sun Wen, Zhao Z. Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement // ISPRS Journal of Photogrammetry and Remote Sensing. 2025. Vol. 222. pp. 167-185.
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TY - JOUR
DO - 10.1016/j.isprsjprs.2025.02.019
UR - https://linkinghub.elsevier.com/retrieve/pii/S0924271625000772
TI - Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement
T2 - ISPRS Journal of Photogrammetry and Remote Sensing
AU - Fan, Chengyan
AU - Mu, Cuicui
AU - Liu, Lin
AU - Tingjun, Zhang
AU - Jia, Shichao
AU - Wang, Shengdi
AU - Sun Wen
AU - Zhao, Zhuoyi
PY - 2025
DA - 2025/04/01
PB - Elsevier
SP - 167-185
VL - 222
SN - 0924-2716
SN - 1872-8235
ER -
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@article{2025_Fan,
author = {Chengyan Fan and Cuicui Mu and Lin Liu and Zhang Tingjun and Shichao Jia and Shengdi Wang and Sun Wen and Zhuoyi Zhao},
title = {Time-Series models for ground subsidence and heave over permafrost in InSAR Processing: A comprehensive assessment and new improvement},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
year = {2025},
volume = {222},
publisher = {Elsevier},
month = {apr},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0924271625000772},
pages = {167--185},
doi = {10.1016/j.isprsjprs.2025.02.019}
}