Active Layer Thickness Retrieval of Qinghai–Tibet Permafrost Using the TerraSAR-X InSAR Technique
Qinghai–Tibet plateau (QTP) is closely related to global climate change, and it has undergone serious permafrost degradation due to global warming in the last decades. It is crucial to measure the active layer thickness (ALT) for characterizing and monitoring the permafrost degradation of...
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Main Authors: | , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.1109/JSTARS.2018.2873219 https://doaj.org/article/b302e03f951d4566b725cf0c35dbbc01 |
| Summary: | Qinghai–Tibet plateau (QTP) is closely related to global climate change, and it has undergone serious permafrost degradation due to global warming in the last decades. It is crucial to measure the active layer thickness (ALT) for characterizing and monitoring the permafrost degradation of QTP. In this paper, an ALT retrieval model based on ground subsidence derived from synthetic aperture radar interferometry (InSAR), land cover types, and groundwater information is proposed. In particular, the surface subsidence is retrieved using the time-series InSAR technique with TerraSAR-X ST mode images. Moreover, groundwater content models with different land covers are constructed based on multilayered assumptions and in situ data. By taking into account the groundwater content profile and land cover types, the ALT is retrieved from deformation with the full season cycle derived by InSAR technique. The experimental results in Beiluhe indicate that the estimated ALT is consistent with field-measured data. The estimated ALT map shows the difference between the alpine meadow and alpine desert areas, with mean ALT of approximately 1.5 m in alpine meadow area and approximately 3 m in alpine desert area. Our results demonstrate that the InSAR technique with high-resolution SAR images can be of great importance for the study of permafrost environments. |
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